{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "<p align=\"center\">\n",
    "    <img src=\"https://github.com/GeostatsGuy/GeostatsPy/blob/master/TCG_color_logo.png?raw=true\" width=\"220\" height=\"240\" />\n",
    "\n",
    "</p>\n",
    "\n",
    "## Subsurface Data Analytics \n",
    "\n",
    "## K-means Clustering\n",
    "\n",
    "\n",
    "### Michael Pyrcz, Associate Professor, University of Texas at Austin \n",
    "\n",
    "#### [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1)\n",
    "\n",
    "### PGE 383 Exercise: K-means Clustering for Subsurface Data Analytics in Python \n",
    "\n",
    "Here's a simple workflow, demonstration of K-means clustering for subsurface modeling workflows. This should help you get started with inferential methods to find patterns in your subsurface data sets.  \n",
    "\n",
    "This workflow is broken up to the primary steps of:\n",
    "\n",
    "* assign initial random prototype with labels\n",
    "\n",
    "* assign samples to the nearest prototype label\n",
    "\n",
    "* update prototype based on centroids of samples belonging to this prototype\n",
    "\n",
    "* iterate until no sample assignments change\n",
    "\n",
    "This allows us to be able to watch the method in action, as opposed to just getting a result.  I think this is more instructive.\n",
    "\n",
    "For this workflow I have modified code from the tutorial provided by Ben Keen as functions to take care of the steps (assign training data to the nearest prototype, update the prototype to the centroid of the assigned data).  The original tutorial is avaiable at [here](http://benalexkeen.com/k-means-clustering-in-python). All I did was specify the method to my data example for clarity, and include the normalized and original data. Appreciation to Ben!\n",
    "\n",
    "#### k-Means Clustering\n",
    "\n",
    "The K-means clustering approach is primaryly applied as an unsupervised method for classification:\n",
    "\n",
    "* **Prototype Method** - represents the training data with number of synthetic cases in the features space. For K-means clustering we assign and iteratively update $K$ prototypes.\n",
    "\n",
    "* **Iterative Solution** - the initial prototypes are assigned randomly in the feature space, the labels for each training sample are updated to the nearest prototype, then the prototypes are adjusted to the centroid of their assigned training data, repeat until there is no further update to the training data assignments.\n",
    "\n",
    "* **Unsupervised Learning** - the training data are not labeled and are assigned $K$ labels based on their proximity to the prototypes in the feature space.  The idea is that similar things, proximity in feature space, should belong to the same category.  \n",
    "\n",
    "* **Feature Weighting** - the procedure depends on the 'distance' between training samples and prototypes in feature space.  Distance is treated as the 'inverse' of similarity. If the features have significantly different magnitudes, the feature(s) with the largest magnitudes and ranges will dominate the process.  One approach is to sandardize / normalize the variables.  Also, by-feature weighting may be applied.  In this demonstration we normalize the features to range from 0.0 to 1.0.\n",
    "\n",
    "* Supervised Learning Variant for Classification of the Feature Space - applies multiple prototypes in each category to then constructs a decision boundary based on nearest prototype.  More prototypes per category results in a more complicated decision boundary in the feature space.  \n",
    "\n",
    "#### Objective \n",
    "\n",
    "In the PGE 383: Subsurface Machine Learning class I want to provide hands-on experience with building subsurface modeling workflows. Python provides an excellent vehicle to accomplish this. I have coded a package called GeostatsPy with GSLIB: Geostatistical Library (Deutsch and Journel, 1998) functionality that provides basic building blocks for building subsurface modeling workflows. \n",
    "\n",
    "The objective is to remove the hurdles of subsurface modeling workflow construction by providing building blocks and sufficient examples. This is not a coding class per se, but we need the ability to 'script' workflows working with numerical methods.    \n",
    "\n",
    "#### Getting Started\n",
    "\n",
    "Here's the steps to get setup in Python with the GeostatsPy package:\n",
    "\n",
    "1. Install Anaconda 3 on your machine (https://www.anaconda.com/download/). \n",
    "2. From Anaconda Navigator (within Anaconda3 group), go to the environment tab, click on base (root) green arrow and open a terminal. \n",
    "3. In the terminal type: pip install geostatspy. \n",
    "4. Open Jupyter and in the top block get started by copy and pasting the code block below from this Jupyter Notebook to start using the geostatspy functionality. \n",
    "\n",
    "You will need to copy the data file to your working directory.  They are available here:\n",
    "\n",
    "* Tabular data - unconv_MV.csv at https://git.io/fjmBH.\n",
    "\n",
    "There are exampled below with these functions. You can go here to see a list of the available functions, https://git.io/fh4eX, other example workflows and source code. \n",
    "\n",
    "#### Install Packages\n",
    "\n",
    "We will include the standard packages for DataFrames and ndarrays and add sci-kit-learn (sklearn) for machine learning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import geostatspy.GSLIB as GSLIB          # GSLIB utilies, visualization and wrapper\n",
    "import geostatspy.geostats as geostats    # GSLIB methods convert to Python        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will also need some standard packages. These should have been installed with Anaconda 3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np                        # ndarrys for gridded data\n",
    "import pandas as pd                       # DataFrames for tabular data\n",
    "import os                                 # set working directory, run executables\n",
    "import matplotlib.pyplot as plt           # for plotting\n",
    "import copy                               # for deep copies\n",
    "import warnings                           # supress all warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Declare Functions\n",
    "\n",
    "The following functions perform the steps required by K-means clustering.\n",
    "\n",
    "* assign the training data to the nearest prototype\n",
    "\n",
    "* update the prototype to the centroid of the assigned training data\n",
    "\n",
    "Don't be concerned if you don't understand the code, we have used some advanced approaches for the benefit of concise code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assignment function to assigned training data to the nearest prototype (code modified from Ben Keen, http://benalexkeen.com/k-means-clustering-in-python/)\n",
    "def assignment(df, centroids):\n",
    "    for i in centroids.keys():\n",
    "        df['distance_from_{}'.format(i)] = (    # use the normalized features and centroids\n",
    "            np.sqrt(\n",
    "                (df['Norm_Porosity'] - centroids[i][2]) ** 2\n",
    "                + (df['Norm_AI'] - centroids[i][3]) ** 2\n",
    "            )\n",
    "        )\n",
    "    centroid_distance_cols = ['distance_from_{}'.format(i) for i in centroids.keys()]\n",
    "    df['closest'] = df.loc[:, centroid_distance_cols].idxmin(axis=1)\n",
    "    df['closest'] = df['closest'].map(lambda x: int(x.lstrip('distance_from_')))\n",
    "    df['color'] = df['closest'].map(lambda x: colmap[x])\n",
    "    return df\n",
    "\n",
    "# Update function to shift the prototype to the centroid of the training data assigned to the prototype (code modified from Ben Keen, http://benalexkeen.com/k-means-clustering-in-python/)\n",
    "def update(k,pormin,pormax,AImin,AImax):\n",
    "    for i in centroids.keys():\n",
    "        centroids[i][2] = np.mean(df[df['closest'] == i]['Norm_Porosity'])\n",
    "        centroids[i][3] = np.mean(df[df['closest'] == i]['Norm_AI'])\n",
    "        centroids[i][0] = centroids[i][2] * (pormax-pormin) + pormin\n",
    "        centroids[i][1] = centroids[i][3] * (AImax-AImin) + AImin\n",
    "    return k"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Set the working directory\n",
    "\n",
    "I always like to do this so I don't lose files and to simplify subsequent read and writes (avoid including the full address each time). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#os.chdir(\"C:/PGE383\")                     # set the working directory with the input data file"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Loading Data\n",
    "Let's load the provided multivariate, spatial dataset '12_sample_data.csv'.  It is a comma delimited file with: \n",
    "\n",
    "* X and Y coordinates ($m$)\n",
    "* facies 0 and 1 \n",
    "* porosity (fraction)\n",
    "* permeability ($mD$)\n",
    "* acoustic impedance ($\\frac{kg}{m^3} \\cdot \\frac{m}{s} \\cdot 10^3$). \n",
    "\n",
    "We load it with the pandas 'read_csv' function into a data frame we called 'df' and then preview it to make sure it loaded correctly.\n",
    "\n",
    "**Python Tip: using functions from a package** just type the label for the package that we declared at the beginning:\n",
    "\n",
    "```python\n",
    "import pandas as pd\n",
    "```\n",
    "\n",
    "so we can access the pandas function 'read_csv' with the command: \n",
    "\n",
    "```python\n",
    "pd.read_csv()\n",
    "```\n",
    "\n",
    "but read csv has required input parameters. The essential one is the name of the file. For our circumstance all the other default parameters are fine. If you want to see all the possible parameters for this function, just go to the docs [here](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html).  \n",
    "\n",
    "* The docs are always helpful\n",
    "* There is often a lot of flexibility for Python functions, possible through using various inputs parameters\n",
    "\n",
    "also, the program has an output, a pandas DataFrame loaded from the data.  So we have to specficy the name / variable representing that new object.\n",
    "\n",
    "```python\n",
    "df = pd.read_csv(\"12_sample_data.csv\")  \n",
    "```\n",
    "\n",
    "Let's run this command to load the data and then this command to extract a random subset of the data.\n",
    "\n",
    "```python\n",
    "df = df.sample(frac=.30, random_state = 73073); \n",
    "df = df.reset_index()\n",
    "```\n",
    "\n",
    "We do this to reduce the number of data for ease of visualization (hard to see if too many points on our plots)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df = pd.read_csv('12_sample_data.csv')    # load our data table\n",
    "df = pd.read_csv('https://raw.githubusercontent.com/GeostatsGuy/GeoDataSets/master/12_sample_data.csv')\n",
    "df = df.sample(frac=.30, random_state = 73073); df = df.reset_index() # extract 30% random to reduce the number of data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Summary Statistics for Tabular Data\n",
    "\n",
    "The table includes porosity (fraction) and acoustic impedance ($\\frac{kg}{m^3} \\cdot \\frac{m}{s} \\cdot 10^3$) that we will work with in the demonstration below.\n",
    "\n",
    "There are a lot of efficient methods to calculate summary statistics from tabular data in DataFrames. The describe command provides count, mean, minimum, maximum, and quartiles all in a nice data table. We use transpose just to flip the table so that features are on the rows and the statistics are on the columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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       "      <td>659.250000</td>\n",
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       "      <td>263.691435</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>242.500000</td>\n",
       "      <td>400.000000</td>\n",
       "      <td>650.000000</td>\n",
       "      <td>980.000000</td>\n",
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       "      <td>144.0</td>\n",
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       "      <td>19.000000</td>\n",
       "      <td>300.000000</td>\n",
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       "      <td>800.000000</td>\n",
       "      <td>979.000000</td>\n",
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       "    <tr>\n",
       "      <th>Facies</th>\n",
       "      <td>144.0</td>\n",
       "      <td>0.659722</td>\n",
       "      <td>0.475456</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
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       "    <tr>\n",
       "      <th>Porosity</th>\n",
       "      <td>144.0</td>\n",
       "      <td>0.190700</td>\n",
       "      <td>0.031972</td>\n",
       "      <td>0.131230</td>\n",
       "      <td>0.166621</td>\n",
       "      <td>0.188733</td>\n",
       "      <td>0.217234</td>\n",
       "      <td>0.256172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Perm</th>\n",
       "      <td>144.0</td>\n",
       "      <td>510.036736</td>\n",
       "      <td>1136.459068</td>\n",
       "      <td>0.039555</td>\n",
       "      <td>6.950509</td>\n",
       "      <td>56.886770</td>\n",
       "      <td>356.658709</td>\n",
       "      <td>7452.343369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AI</th>\n",
       "      <td>144.0</td>\n",
       "      <td>3746.825725</td>\n",
       "      <td>793.196589</td>\n",
       "      <td>1961.600397</td>\n",
       "      <td>3167.631744</td>\n",
       "      <td>3668.526774</td>\n",
       "      <td>4244.264532</td>\n",
       "      <td>6194.573653</td>\n",
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       "            count         mean          std          min          25%  \\\n",
       "index       144.0   261.048611   136.830267     1.000000   144.750000   \n",
       "Unnamed: 0  144.0   440.291667   245.046303     2.000000   217.250000   \n",
       "X           144.0   449.375000   263.691435     0.000000   242.500000   \n",
       "Y           144.0   542.979167   289.228936    19.000000   300.000000   \n",
       "Facies      144.0     0.659722     0.475456     0.000000     0.000000   \n",
       "Porosity    144.0     0.190700     0.031972     0.131230     0.166621   \n",
       "Perm        144.0   510.036736  1136.459068     0.039555     6.950509   \n",
       "AI          144.0  3746.825725   793.196589  1961.600397  3167.631744   \n",
       "\n",
       "                    50%          75%          max  \n",
       "index        270.000000   381.250000   478.000000  \n",
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       "Y            579.000000   800.000000   979.000000  \n",
       "Facies         1.000000     1.000000     1.000000  \n",
       "Porosity       0.188733     0.217234     0.256172  \n",
       "Perm          56.886770   356.658709  7452.343369  \n",
       "AI          3668.526774  4244.264532  6194.573653  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().transpose()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The two features are quite incompatible.  They have dramatically different:\n",
    "\n",
    "* magnitudes / averages\n",
    "\n",
    "* variances / ranges\n",
    "\n",
    "We should make a normalized version of each.  We will scale the variables to range from 0 to 1.  \n",
    "\n",
    "* There is no distribution shape change.\n",
    "\n",
    "We will use these normalized values for calculating distance in our workflow:\n",
    "\n",
    "* to remove the influence of magnitude and range on our similarity calculation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "pormin = df['Porosity'].min(); pormax = df['Porosity'].max()\n",
    "AImin = df['AI'].min(); AImax = df['AI'].max()\n",
    "\n",
    "df['Norm_Porosity'] = (df['Porosity']-pormin)/(pormax - pormin)\n",
    "df['Norm_AI'] = (df['AI']-AImin)/(AImax - AImin)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's confirm that our normalized porosity and acoustic impedance now range between 0 and 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
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       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Porosity</th>\n",
       "      <td>144.0</td>\n",
       "      <td>0.190700</td>\n",
       "      <td>0.031972</td>\n",
       "      <td>0.131230</td>\n",
       "      <td>0.166621</td>\n",
       "      <td>0.188733</td>\n",
       "      <td>0.217234</td>\n",
       "      <td>0.256172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Perm</th>\n",
       "      <td>144.0</td>\n",
       "      <td>510.036736</td>\n",
       "      <td>1136.459068</td>\n",
       "      <td>0.039555</td>\n",
       "      <td>6.950509</td>\n",
       "      <td>56.886770</td>\n",
       "      <td>356.658709</td>\n",
       "      <td>7452.343369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AI</th>\n",
       "      <td>144.0</td>\n",
       "      <td>3746.825725</td>\n",
       "      <td>793.196589</td>\n",
       "      <td>1961.600397</td>\n",
       "      <td>3167.631744</td>\n",
       "      <td>3668.526774</td>\n",
       "      <td>4244.264532</td>\n",
       "      <td>6194.573653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Norm_Porosity</th>\n",
       "      <td>144.0</td>\n",
       "      <td>0.475986</td>\n",
       "      <td>0.255894</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.283258</td>\n",
       "      <td>0.460240</td>\n",
       "      <td>0.688350</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Norm_AI</th>\n",
       "      <td>144.0</td>\n",
       "      <td>0.421743</td>\n",
       "      <td>0.187385</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.284914</td>\n",
       "      <td>0.403245</td>\n",
       "      <td>0.539258</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               count         mean          std          min          25%  \\\n",
       "index          144.0   261.048611   136.830267     1.000000   144.750000   \n",
       "Unnamed: 0     144.0   440.291667   245.046303     2.000000   217.250000   \n",
       "X              144.0   449.375000   263.691435     0.000000   242.500000   \n",
       "Y              144.0   542.979167   289.228936    19.000000   300.000000   \n",
       "Facies         144.0     0.659722     0.475456     0.000000     0.000000   \n",
       "Porosity       144.0     0.190700     0.031972     0.131230     0.166621   \n",
       "Perm           144.0   510.036736  1136.459068     0.039555     6.950509   \n",
       "AI             144.0  3746.825725   793.196589  1961.600397  3167.631744   \n",
       "Norm_Porosity  144.0     0.475986     0.255894     0.000000     0.283258   \n",
       "Norm_AI        144.0     0.421743     0.187385     0.000000     0.284914   \n",
       "\n",
       "                       50%          75%          max  \n",
       "index           270.000000   381.250000   478.000000  \n",
       "Unnamed: 0      459.000000   659.250000   825.000000  \n",
       "X               400.000000   650.000000   980.000000  \n",
       "Y               579.000000   800.000000   979.000000  \n",
       "Facies            1.000000     1.000000     1.000000  \n",
       "Porosity          0.188733     0.217234     0.256172  \n",
       "Perm             56.886770   356.658709  7452.343369  \n",
       "AI             3668.526774  4244.264532  6194.573653  \n",
       "Norm_Porosity     0.460240     0.688350     1.000000  \n",
       "Norm_AI           0.403245     0.539258     1.000000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().transpose()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's extract the porosity and acoustic impedance features and then look at the resulting DataFrame to ensure that we loaded and reformatted as expected. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Porosity</th>\n",
       "      <th>AI</th>\n",
       "      <th>Norm_Porosity</th>\n",
       "      <th>Norm_AI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.139637</td>\n",
       "      <td>4747.274043</td>\n",
       "      <td>0.067289</td>\n",
       "      <td>0.658089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.170732</td>\n",
       "      <td>4535.625583</td>\n",
       "      <td>0.316164</td>\n",
       "      <td>0.608089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.244345</td>\n",
       "      <td>2696.102930</td>\n",
       "      <td>0.905345</td>\n",
       "      <td>0.173519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.167125</td>\n",
       "      <td>5500.997419</td>\n",
       "      <td>0.287294</td>\n",
       "      <td>0.836149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.216253</td>\n",
       "      <td>3959.934912</td>\n",
       "      <td>0.680501</td>\n",
       "      <td>0.472088</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Porosity           AI  Norm_Porosity   Norm_AI\n",
       "0  0.139637  4747.274043       0.067289  0.658089\n",
       "1  0.170732  4535.625583       0.316164  0.608089\n",
       "2  0.244345  2696.102930       0.905345  0.173519\n",
       "3  0.167125  5500.997419       0.287294  0.836149\n",
       "4  0.216253  3959.934912       0.680501  0.472088"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_subset = df.iloc[:,[5,7,8,9]]              # extract Porosity and AI for a simple 2D example\n",
    "df_subset.head()                          # preview the new DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Some Parameters\n",
    "\n",
    "From the summary statistics we can assign a reasonable minimum and maximum for each feature.  \n",
    "\n",
    "* We will use this for plotting.\n",
    "\n",
    "We will also set the random number seed to ensure that the program does the same thing everytime it is run.\n",
    "\n",
    "* Change the seed number for a different result\n",
    "\n",
    "We will set the number of prototypes / clusters, *K*\n",
    "\n",
    "We define a dictionary with the color code for each cluster, $k = 1,\\ldots,K$.  Given 7 codes currently, there will be an error if $K$ is set larger than 7.  Add more color codes to the dictionary to allow for mor categories."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "por_min = 0.12; por_max = 0.28\n",
    "AI_min = 1500; AI_max = 6500\n",
    "np.random.seed(712)\n",
    "K = 3                                     # number of prototypes\n",
    "colmap = {1: 'r', 2: 'g', 3: 'b', 4: 'm', 5: 'c', 6: 'k', 7: 'w'}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualization of Training Data\n",
    "\n",
    "In this exercise, we want to use K-means clustering provide facies based on acoustic impedance and porosity predictor features. \n",
    "\n",
    "* This allows use to group rock with similar petrophysical and geophysical properties.\n",
    "\n",
    "Let's start by looking at the scatterplot of our training data features, porosity and acoustic impedance.  \n",
    "\n",
    "* We will look at the data in original units and normalized units through this entire exercise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# scatter plot our training data  \n",
    "plt.subplot(121)\n",
    "plt.scatter(df_subset['Porosity'], df['AI'], c=\"black\", alpha = 0.4, linewidths=1.0, edgecolors=\"black\")\n",
    "plt.title('Acoustic Impedence vs. Porosity'); plt.xlabel('Porosity (fraction)'); plt.ylabel('Acoustic impedance (kg/m^3 x m/s x 10^3)')\n",
    "plt.xlim(por_min, por_max)\n",
    "plt.ylim(AI_min, AI_max)\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.scatter(df_subset['Norm_Porosity'], df['Norm_AI'], c=\"black\", alpha = 0.4, linewidths=1.0, edgecolors=\"black\")\n",
    "plt.title('Normalized Acoustic Impedence vs. Porosity'); plt.xlabel('Porosity (normalized)'); plt.ylabel('Acoustic impedance (normalized)')\n",
    "plt.xlim(0.0,1.0)\n",
    "plt.ylim(0.0,1.0)\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=2.0, top=1.0, wspace=0.2, hspace=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Initialize K Prototypes\n",
    "\n",
    "First we will assign K prototypes in the feature space randomly.\n",
    "\n",
    "* for K prototypes assign a random porosity and acoustic impedance\n",
    "\n",
    "* don't worry, these prototypes won't make much sense initially, but they will improve\n",
    "\n",
    "We will do this and then visualize the prototypes as red, green, blue etc. dots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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x1hcRkdaitD770prWq8muiNTEzJYC9wH/7u6PJh2PVC7s97GFoClfXH1jpIHM7Aoz+72Z3VfidTOzi81sjZmtNrPjGh2jiGSH0vrsS3Narya7IiIiTSYcrOVPBHPcvbzI628g6K/4BoJRQr/s7nENbiMiIlKSakhFRESajLv/lGBwiVLOJsisurvfQTCnXtSBYUREROpGGVIREZHWM4vdJ0DfSPnJ1EVERGLRtIMa/dmf/ZkfcsghSYchIiIpc9ddd/3B3WckHUfCik3oXrQPj5ktIpjQnn322ef4I488Ms64GmZoaIjJk/ecdvC5556jq6srgYhERLKt2vS1aTOkhxxyCKtWrUo6DBERSRkzW5d0DCmwETgo73k3wRxye3D3y4HLAebMmePNkrb29/czNjZGR0fHrmWjo6O0tbXR19eXXGAiIhlVbfqqJrsiIiKt5zrgPeFouycBT7n740kH1Ui9vb2MjIwwOjrKzp07GR0dZWRkhN7e3qRDExFpKcqQioiINBkz+zbwS+AlZrbRzBaa2blmdm64yvXAI8Aa4L+ADyUUamJ6enpYuHAhbW1tDA0N0dbWxsKFC+np6Uk6NBGRltK0TXZFRERalbufM87rDpzXoHBSq6enRxlQEZGEqYZUREREREREEqEMqYiIiIiIiCRCGVIRERERERFJhDKkIiIiIiIikghlSEVERERERCQRypCKiIiIiIhIIpQhFRERERERkUQoQyoiIiIiIiKJ2CvpAERERKQ5DA4OsmLFCoaGhpg1axa9vb309PQkHZaIiKSYMqQtQjcJIiISp8HBQQYGBujs7KS7u5stW7YwMDDAwoULld6IiEhJarLbAnI3CWNjY3R3dzM2NsbAwACDg4NJhyYiIk1ixYoVdHZ20tHRwYQJE+jo6KCzs5MVK1YkHZqIiKSYMqQtQDcJIiISt6GhIdrb23db1t7ezvDwcEIRiYhIFihD2gJ0kyAiInGbNWsWW7Zs2W3Zli1b6OrqSigiERHJAmVIW4BuEkREJG69vb2MjIwwOjrKzp07GR0dZWRkhN7e3qRDExGRFFOGtAXoJkFEROLW09PDwoULaWtrY2hoiLa2Ng1oJCIi49Iouy0gd5OQG2W3q6uL+fPn6yZBRETqqqenR2mLiIhURBnSFqGbBBERERERSRs12RUREREREZFEKEMqIiIiIiIiiVCGVERERERERBIRa4bUzDrM7Ltm9pCZPWhmrzazTjO72cwGw7/T89b/pJmtMbOHzex1ecuPN7N7w9cuNjOLM24RERERERGJX9w1pF8GfuzuRwKvBB4EPgGsdPceYGX4HDN7GfBO4Cjg9cClZjYxfJ9+YBHQEz5eH3PcIiIiIiIiErPYMqRm1g68BhgAcPdn3X0UOBu4MlztSuAt4f9nA1e7+3Z3fxRYA5xgZgcC7e7+S3d34Jt524iIiIiIiEhGxTnty2HAk8DXzeyVwF3ABcBMd38cwN0fN7P9w/VnAXfkbb8xXPZc+H/h8tQbHBzcNffnrFmz6O3t1dQrIiKSWffeez8TJkxk9uyXsmzZYhYsOCfpkEREJOPibLK7F3Ac0O/uxwJPEzbPLaFYv1Avs3zPNzBbZGarzGzVk08+WWm8dTU4OMjAwABjY2N0d3czNjbGwMAAg4ODicYlIiJSrWefnY37M6xbdwmLFi1m+fJvJx2SiIhkXJwZ0o3ARnf/Vfj8uwQZ1E1hM1zCv7/PW/+gvO27geFweXeR5Xtw98vdfY67z5kxY0bddqQaK1asoLOzk46ODiZMmEBHRwednZ2sWLEi0bhERESq1wZMAuaydesAixcvSzogERHJuNgypO7+BLDBzF4SLpoHPABcB7w3XPZe4Afh/9cB7zSzvc3sUILBi34dNu8dM7OTwtF135O3TWoNDQ3R3t6+27L29naGh4vmpUVERDLmFNavfzDpIEREJOPi7EMKcD6w3MwmA48A7yfIBF9jZguB9cDbANz9fjO7hiDTugM4z92fD9+nD/gGMBW4IXyk2qxZs9iyZQsdHR27lm3ZsoWurq7kghIREambnzF79kuTDkJERDIu1gypu98DzCny0rwS6y8D9mj/4+6rgJfXNbiY9fb2MjAwAAQ1o1u2bGFkZIT58+cnHJmIiEi1xgjGGvwZ06YtZNkyNdkVEZHaxD0Pacvq6elh4cKFtLW1MTQ0RFtbGwsXLtQouyIiklmTJ6/HbAoHH3w+l1++TKPsiohIzcatITWzOcCpQBewDbgPWOHuIzHHlnk9PT3KgIqISNN4xSuOYtWqVUmHISIiTaRkDamZvc/MfgN8kqDv5sMEI+KeAtxsZlea2ezGhCkiIiIiIiLNplwN6T7Aye6+rdiLZnYMwUi462OIS0RERFJucHCQFStWMDQ0xKxZs+jt7VXLIBERqUjJGlJ3/2qpzGj4+j3uvjKesERERCTNBgcHGRgYYGxsjO7ubsbGxhgYGGBwcDDp0EREJEPKNdk1M3u7mb0t/H+emV1sZh8yMw2GJCIi0sJWrFhBZ2cnHR0dTJgwgY6ODjo7O1mxYkXSoYmISIaUy1h+FXg78G7gW8C5wCrgNcB/xB9aNixf/m0OOeTlTJgwkUMOeTnLl3876ZBERERiNzQ0RHt7+27L2tvbGR4eTigiERHJonJ9SE9191eY2STgCeBAd3/WzK4C7m5MeOm2fPm3WbRoMVu3DgCnsG7dz1i0aCGAhsIXEZGmNmvWLLZs2UJHR8euZVu2bKGrqyu5oEREJHPK1ZDuAHD354A73f3Z8PkO4PkGxJZ6ixcvCzOjc4FJwFy2bh1g8WJNFC4iIs2tt7eXkZERRkdH2blzJ6Ojo4yMjNDb25t0aCIikiHlMqRPmNm+AO7++txCMzsAeDbuwLJg/foHCWbByXdKuFxERKR59fT0sHDhQtra2hgaGqKtrY2FCxdqlF0REalIySa77n5WiZfGgDfFE062zJ79Utat+xlBDWnOz5g9+6VJhSQiItIwPT09yoBmhKboEZG0qni0XHd/2t1/H0cwWbNs2WKmTVsI3AI8B9zCtGkLWbZsccKRiYiIiAQ0RY+IpFnZDKmZ7WVmVzQqmKxZsOAcLr98GQcffD5mUzj44PO5/PJlGtBIREREUkNT9IhImpVsshv2H/0f4LrGhZM9CxacowyoiIhIirV6c9WhoSG6u7t3W9be3s7Q0FBCEYmIvKBcDemtwPXu3t+gWERERETqSs1VX5iiJ5+m6BGRtCg3D+l+wIZGBdIsWr0UVkREJE3ym6sCu/6uWLGiZdLn3t5eBgYGgKBmdMuWLYyMjDB//vyEIxMRKV9D+hrgE2Z2dqOCyTqVwoqIiKTL0NAQ7e3tuy1rb29neHg4oYjiNzg4SH9/P0uWLKG/P2jopil6RCStyk378riZnQFcBfygcSFll0phRURE0iXXXDWXJkNzN1fNFY53dnbS3d3Nli1bGBgYYOHChfT19SUdnojIHsqOsuvuY4Dac0TUiqWwIiIiadbb28vIyAijo6Ps3LmT0dFRRkZG6O3tTTq0WGhEXRHJmnHnIXX3HY0IpBlo0AAREZF06enpaanmqiocF5GsKTfty37AJ4G3ADPCxb8naL77OXcfjTu4rNGgAfWlAaJERKpnZq8HvgxMBL7m7p8reH0/4L+B2QT3A5939683PNAG6OnpaZn0o9WaKItI9pWrIb0G2Ayc7u4vcvcXAXPDZf/TiOCyptVKYeOkAaJERKpnZhOBrwJnAS8DzjGzlxWsdh7wgLu/Ejgd+IKZTW5ooFJ3rdZEWUSyr9y0L4e4+7/lL3D3J4B/M7MPxBtWdrVSKWycNECUiEhNTgDWuPsjAGZ2NXA28EDeOg60mZkB+wIjgLrpZFyucDzXwqirq4v58+cr7RSR1CqXIV1nZh8DrnT3TQBmNhN4H5qfNNWaoanr0NAQ3d3duy1rb29naGgooYhERDJlFrun1RuBEwvW+QpwHTAMtAHvcPedhW9kZouARQCzZ8+OJVipLxWOi0iWlGuy+w7gRcBtZjZiZiPArUAn8PYGxCZVaJamrhogSkSkJlZkmRc8fx1wD9AFHAN8xczaC9bB3S939znuPmfGjBmFL4uIiNSk3Dykm4GPhw/JiCw3dc2v2Z00aRJDQ0O8+MUv1gBRIiKV2wgclPe8m6AmNN/7CQYpdGCNmT0KHAn8ujEhioiIlG+yW5KZvb9ZR+LLuqw2dS02kbeZsXXrVsbGxtQHRkSkMncCPWZ2KDAEvBN4V8E664F5wO1hl5yXAI80NEoBmqOrjYhItarKkAKfBZQhTaGsDvderGb38MMPp62tjb6+vmSDExHJGHffYWZ/B9xIMO3LFe5+v5mdG75+GbAU+IaZ3UvQxPfj7v6HxIJuUcUKZAcGBjRKv4i0jHLzkK4u9RIwM55wpFZZnQs1qzW7IiJxM7PpBP08twGPFRt4qBh3vx64vmDZZXn/DwNn1jFUqUKWu9qIiNRDuRrSmQQDHmwuWG7AL2KLSGqS1eHes1qzKyISBzPbj2Ce0HOAycCTwBRgppndAVzq7rckGKLUiQpkRaTVlcuQ/gjY193vKXzBzG6NKyCpXRaHe89qza6ISEy+C3wTONXdR/NfMLPjgXeb2WHuPpBEcFI/KpAVkVZXctoXd1/o7j8r8VrhwAgiNcnV7La1tTE0NERbW5v6z4hIy3L3M9z9W4WZ0fC1u9z9I8qMNofe3l5GRkYYHR1l586djI6OMjIyQm9vb9KhiYg0RLWDGonUXRZrdkVE4mBmx5V73d1/06hYJF5Z7WojIlIvypCKiIikzxfCv1OAOcBvCcZwOBr4FXBKQnFJDFQgKyKtTBlSqSvNpSYiUjt3nwtgZlcDi9z93vD5y4ELk4xNRESknkr2Ic0xs5cVWXZ6HMFItuXmUhsbG6O7u5uxsTEGBgYYHBxMOjQRkaw6MpcZBXD3+4BjkgtHRESkvsbNkALXmNnHLTDVzC4B/jXuwCR78udSmzBhAh0dHXR2drJixYqkQxMRyaoHzexrZna6mZ1mZv8FPJh0UCIiIvUSJUN6InAQwdyjdwLDwMlxBiXZNDQ0RHt7+27L2tvbGR4eTigiEZHMez9wP3AB8BHggXCZiIhIU4jSh/Q5YBswlWBwhUfdfWesUUkmaS41EZH6cvdnzOwy4Hp3fzjpeEREROotSg3pnQQZ0lcRjOp3jpl9N9aoJJM0l5qISH2Z2ZuBe4Afh8+PMbPrEg2qzgYHB+nv72fJkiX09/dr3AERkRYTJUO60N0/7e7PufsT7n428IO4A5Psyc2l1tbWxtDQEG1tbSxcuFCj7IqIVO8zwAnAKIC73wMcklw49aXB8EREZNwmu+6+qsiyb8UTjmSd5lITEamrHe7+lJklHUcs8gfDA3b9XbFihdKSBtKUbSKSJM1DKiIikl73mdm7gIlm1gN8mGCQwaYwNDREd3f3bsva29sZGhqK7TOV+dpdrpa6s7OT7u5utmzZwsDAgFo4iUjDRGmyKyIiIsk4HzgK2A5cBTxFMNpuU8gNhpcvzsHw1ER4T5qyTUSSNm4NqZntA2xz951mdgRwJHCDuz8Xe3QtQqW1IiJSwvHAp919cW6BmR0H/Ca5kOqnt7eXgYEBIKgZ3bJlCyMjI8yfPz+Wz1MT4T0lUUudJN1ziaRPlBrSnwJTzGwWsJJg/rNvxBlUK1FprYiIlHEj8BMzm5m37GtJBVNvjR4MT/Nl76nRtdRJ0j2XSDpF6UNq7r7VzBYCl7j7/zOzu+MOrFWotFZERMp4GPh34FYzW+juvwCaaoSjRg6Gp/my99ToWuok6Z5LJJ2i1JCamb0aWAD8X7hMgyHViUprK6c560Skhbi7/wh4M/AVM/s7wBOOKbM0X/aeWmnKNt1ziaRTlIzlR4BPAte6+/1mdhhwS6xRtZA4S2ubsZ+ERgMUkRZjAO4+aGanAl8Hjk42pOzKZb5yaWNXVxfz58+va/qRxrR3vJhaZco21ZCLpJO5Fy9oNbNPAj9296qb55rZY8AY8DzBXGpzzKwT+A7BxN6PAW939815n7kwXP/D7n5juPx4gn6rU4HrgQu8VOChOXPm+KpVe0yhmjr5Gaz8pjK1ZrDiet+k9ff3MzY2tltiMjo6SltbG319fckFFkEab1JEWpGZ3eXuc5KOo1pmNtvd1yfx2VlJW5OSxrQ3jTElRcdCJF7Vpq/lmuw+ClxgZneb2TfM7B1mNr2K2Oa6+zF5wX0CWOnuPQSDJH0CwMxeBryTYHj71wOXmtnEcJt+YBHQEz5eX0UcqRRXU5lmHcY9q81tNJCCiFTCzD4W/r248AFcmHB4UkKSaW+p7izNej9QjVZqniySJSWb7Lr71cDVAGZ2LEEm8HthJnEFQe3pr6v4zLOB08P/rwRuBT4eLr/a3bcDj5rZGuCEsJa13d1/GcbyTeAtwA1VfHYqxdFUplmHcc9qcxsNpCAiFXow/HtXolFIRZJKe8t1Z2nW+4FqtUrzZJEsiTQ4Udhs927gX82sHTgD+CAwXobUgZvMzIH/dPfLgZnu/nj4vo+b2f7hurOAO/K23Rguey78v3D5HsxsEUFNKrNnz46ya00rqxm3fMWauGZ1NEDdEIhIJdz9h+HfK5OORaJLKu0tV+jZDPcDItLcxh1l18wmmtmbzezDZvb3BBnRg919UYT3P9ndjwPOAs4zs9eU+6giy7zM8j0Xul/u7nPcfc6MGTMihNe86j2SYKNHti3VxBXIZHObVprnTURqZ2Y/NLPrSj2Sjk+KS2oU33LdWTSysIikXZQa0h8CzwD3AjvDZZGGnHf34fDv783sWuAEYJOZHRjWjh4I/D5cfSNwUN7m3cBwuLy7yHIpo54jCcY9sm2xmtBypb19fX2pz4AWymrNrogk5vNJByCVa8QovsWUqwVNKiYRkaiiZEi73b3iIebNbB9ggruPhf+fCfwzcB3wXuBz4d8fhJtcB1xlZl8EuggGL/q1uz9vZmNmdhLwK+A9wCWVxtOK6tVPIs7+j6Uyu1u2bOHoo3c/7bLcxFU3BCJSCXe/LekYpDpJ9FEcr9BT/SZFJM2iZEhvMLMz3f2mCt97JnCtmeU+5yp3/7GZ3QlcY2YLgfXA2wDCOU6vAR4AdgDnufvz4Xv18cK0LzfQRAMaZUGc/R9LZXbXrVvXdH1edEMgIpUysx7gX4GXAVNyy939sMSCktRRoaeIZFmUDOkdBBnLCQQDDBng7t5ebiN3fwR4ZZHlfwTmldhmGbCsyPJVwMsjxCoxiHNAhFKZ3enTpzMyMrLruZq4ikiL+jrwGeA/gLnA+yk+toK0OBV6ikhWjTuoEfAF4NXANHdvd/e28TKj0lziHBCh1GA/r3jFKzI5eJGISJ1NdfeVgLn7One/CHhtwjGJiIjUTZQa0kHgPnePNJCRNJ84mwKV6/ei0l4REZ4JWygNmtnfAUPA/uNsIxKbYgMRKq0WkVrYePlMM/sGcBhBv83tueXu/sVYI6vRnDlzfNWqVUmHIRHkErfh4WG6urqUuIlIrMzsLnefk3QcUZjZq4AHgQ5gKbAf8P/c/Y5y28VFaWtryx+IML8QWS2YRASqT1+j1JA+Gj4mhw+RulJNqIhIce5+Z/jvnwj6jza1m2++mSuuuIINGzZw0EEH8YEPfIAzzjgj6bAyI+7ayzhH3ReR1jVuhtTdP9uIQETqRc2JRKRZmNkcYDFwMHlpdjXTsaXdzTffzKc+9Sk6OzuZPXs2o6OjfOpTnwIomSnV9f4Fcc8ZDvGOui8irSvKoEYisRscHKS/v58lS5bQ39/P4OBg1e8zMDDA2NgY3d3djI2NMTAwUPX7iYgkbDnBSLt/BfxF3iMRQ0NDNV2jy7niiivo7Oyks7OTCRMm7Pr/iiuuKLq+rve7y6+9nDBhAh0dHXR2drJixYq6fUapgQizPCWbiCRPGdKUufnmmznnnHM45ZRTOOecc7j55pv3WKdembe0qOdNRSMSZBGRBnrS3a9z90fDUXbXufu6pIKZPHlybBm/DRs27Da9GARNQjdu3Fh0/Xpc75spPR0aGqK9ffdJENrb2xkeHq7bZ8Q56r6ItK6qMqRmpr6k1D8hyzVXeuqpp5g9ezZPPfUUn/rUp3bLlDZjiXA9M5GNSJBFRBroM2b2NTM7x8z+MvdIMqC4CvoOOuggRkdHd1s2Ojq6RxPRnFqv982Wnjai9jI36r6mZBORehq3D6mZ3Qq8z90fC5+fAPwX8MpYI0u5OPpq5DdXAnb9veKKK3b1n6lmQIG097GpZ5+UXIKcX8qu5kQikmHvB44EJgE7w2UOfC+xiIin3+AHPvCBXX1GOzo6dtW+ffSjHy26fq3X+2YboKfcNGr1pIEIRaTeotSQ/ivwYzP7kJktAy6jBUb6G08cTUOjNFeqtEQ4CyXA9SzVVXMiEWkyr3T3Oe7+Xnd/f/j4QNJBxVHQd8YZZ7B06VL2228/NmzYwH777cfSpUtLDmhU6/W+2VrUqPZSRLIqyii7N5rZucDNwB+AY939idgjS7k4RprLNVfK1YzCns2VKi0RzkIJcD1LdXMJcq5GuKuri/nz56dmX0VEKnSHmb3M3R9IOpCcXMav3jVvEGRKo07zUuv1vhlb1Kj2UkSyKEqT3U8BbwdeAxwN3Gpm/+Du/xd3cGkWR0IWpblSpZm3LAzRXu9MpBJkEWkipwDvNbNHge2AAZ7UtC/PPfccbW1tqSnoq+V636gmriIiUt64GVLgz4AT3H0b8Esz+zHwNaClM6RxJGS5UuHcpODd3d189KMf3a20uNLMW1ZKgJWJFBHZnZkZ8LdAYqPqFurq6qKvry/pMOpCLWpERNLB3D3pGGIxZ84cX7VqVayfkRssaHh4mK6urtQNFgS7D76Un3FWvxIRaVVmdpe7z0k6jijCWI9POo6cRqStIiJplPZBQtOg2vQ1Sg2plJCFWj2VAIvUTomQJOgOM3uVu9+ZdCAiIq0qjtk15AXKkLaALGScRdJKiZAkbC5wrpk9BjxNwn1IRURaURYGCc0yZUhFRMpQIiQJOyvpAEREWl0WBgnNspLzkFrg7Wb2tvD/eWZ2cTgfaZT5S0VEMq/Z5iqUbHH3dUAH8BfhoyNcJiIiDZIbJDRfGgcJzapyNaRfBfYHJgNnA3sDPwTeALwEuCD26ETGob59EresjFQtzcnMLgD+BvheuOi/zexyd78kwbCkySltFdmdpomKV7mazlPd/a3AXxE0GVrg7t8E/hp4bSOCEykn17dvbGyM7u5uxsbGGBgYYHBwMOnQpIn09vYyMjLC6OgoO3fu3DU/cG9vb9KhSWtYCJzo7p92908DJxFkUMdlZq83s4fNbI2ZfaLEOqeb2T1mdr+Z3VbHuCWjlLaK7Ck3SGhbWxtDQ0O0tbVpLIk6KldDugPA3Z8zszvd/dnw+Q4ze74h0YmUob590ggaqVoSZkB+mvt8uKz8RmYTCVo6nQFsBO40s+vc/YG8dTqAS4HXu/t6M9u/noFLNiltFSlOg4TGp1yG9Akz29fd/+Tur88tNLMDgGfjD02kPHUwl0ZRIiQJ+jrwKzO7Nnz+FmAgwnYnAGvc/REAM7uaoPvNA3nrvAv4nruvB3D339craMkupa0i0mglM6TuXmpkvzHgTfGEI2mS9j4k6tsnIs3O3b8YNqU9maBm9P3ufneETWcBG/KebwROLFjnCGCSmd0KtAFfDrvmSIXSnl5WQmmriDRaxaPluvvTKkVtfkn2IRkcHKS/v58lS5bQ39+/x2fmXr/33ntZuXIlg4OD6tsnIs3sHuC7wLXAH81sdoRtijXr9YLnewHHA28EXgd8ysyO2OONzBaZ2SozW/Xkk09WFHgraLY+l+o3LyKNVnYeUjPbC7jc3T/QoHgkJRrRh6RYiTLAwMAAnZ2ddHd3s2XLFgYGBnZ1HM8l/J2dnRx99NHss88+rF69mqeffppXvOIV6tsnIk3FzM4HPgNs4oX+ow4cPc6mG4GD8p53A4VzFW0E/uDuTwNPm9lPgVcCv8tfyd0vBy4HmDNnTmGmtuWluc9l1JrbwvXmzZvHmjVr1G9eRBqiZIbUzPYF/ge4rnHhSFrE3YckP2OZn/GcOnVq2YS9MOHv6elhxowZtLW10dfXV5fYRERS5ALgJe7+xwq3uxPoMbNDgSHgnQR9RvP9APhKWPg8maBJ73/UGG/LSbLPZbkMZ6l0tnBk0GLrrVy5UiOIikjDlGuyeytwvbv3NygWSZH8CYA3bdrEbbfdxjXXXMOaNWvq0gwpP2M5YcIEOjo66Ozs5I477qC9vX23ddvb2xkeDgr2h4aGyr4uItJkNgBPVbqRu+8A/g64EXgQuMbd7zezc83s3HCdB4EfA6uBXwNfc/f76hZ5i8hPL3Ma0edyvKbCpdLZFStW7PY+UdcTEYlLuSa7+7H7gAjSQnITAD/55JOsXr2aiRMnMnnyZLq7u4uWsFaqVIkyUHYwhWYabCFtg2CkLR4RAeAR4FYz+z9ge26hu39xvA3d/Xrg+oJllxU8/3fg3+sTamvKTy+HhoZ44oknmDRpEhdeeGGsnzteU+GoNbcaVVdEklauhvQ1wCfM7OxGBSPpkZt7cePGjezYsYPp06dz8skn09PTU5eS01IlyieeeGLZwRSyOthC4UBNN998c6oGwWi2QTlEmsh64GaCJrVteQ9JiZ6eHubNm8fq1avZtGkTM2fO5Oijj9416F5cxmsxFLXmNqkaXhGRnHLTvjxuZmcAVxH0M5EW09PTw+GHH85pp53GhAkvlF3Uo+Q0V6Kce78tW7YwMjLCwoULAbjqqqu49dZbcXdOPPGFmQpyGeVcTV4WBlso1j/n85//PEcffXRqBsFI86AcIq3M3T+bdAwyvjVr1jBv3rzdWu+Mjo7Geg0dr8VQqXR2/vz5u71P1PWybrz+tllsIZTVuEUKlZ32xd3HgOa6IklF4io5zWUs29raGBoaoq2tbbdmwNu2beP000/n7LPPZtq0abvV1vX09NDX18fSpUvp6+tL/cW3WP+cHTt27JGpT7IvrPrmiqSLmV1uZq8o8do+ZvYBM1vQ6LikuCSuoeO1GBovnc2Jul6WlWsFlNUWQlmNW6SYstO+wK6BEaTOslKqFWfJaU9PT9F9jru2rtHHvlj/nP33359NmzbttizJJlLN1DdXpElcSjAv6CuA+4AngSlAD9AOXAEsTy48yZfENTRKi6Hc/7l1ct1timVK03gPUi/l7iuATLYQUssmaSZla0gBzOxNZna3mY2Y2RYzGzOzLeNtJ6VlqVQriZLTOEuakzj2xWqZu7u72WuvvVLTFzarfXNFmpW73+PubwdeBXwVuJ1gGrYPuvsr3f3L7r697JtIwyR1DR2vxVCW7jfiVO6+IqsthLIat0gx49aQAl8C/hK41901IXYdZK1Uq9Elp3GWNCdx7IvVMk+cOJELL7wwNROPZ7FvrkgrcPc/EUzD1jSy0kKoEmm9hiZ5v5Gm73m8+4osthBSyyZpJlEypBuA+5QZrZ9mHmJ9cHCQq666ijvuuAOAE088kQULFlSUCMXZTDiJY1/uRuWMM86I7XMr1exNtkQkecUGeavHVGJpkMZraFL3G2n7nse7r8jioE6tMhiVtIYoGdKPAdeb2W1UOAeaFNespVo333wzn/3sZ9mwYQPTp09nxowZ3HrrrQwPD/OP//iPkROhSkqaKy2BTerYp/FGRUSk0bLWQijrkkrz0vY9j3dfkcba7fGktVZepBpRMqTLgD8RDKYwOd5wWkOxUq01a9Ywa9YslixZknjTlmoMDg7y+c9/ntHRUTo7OzEzhoeH6e7u5sknn6w4EYqSgaumBFYliiKSRWa2j7s/nXQctWrmFkJplFSal8bvudx9RVYLjbMat0ihcQc1Ajrd/S/d/TPu/tncI/bImljhQEFbt27FzJg6dWpmBx1YsWIFO3YEAzJPnjyZSZMmsffee7N582a2b98eSyf7YtOpdHZ27ho1r5hWGN5eRJqHmf25mT0APBg+f6WZXZpwWFWLayqxrBscHOSzn/0sZ511FmeddRYXXXRRXe4Bkkrz9D2LSCWi1JCuMLMz3f2m2KNpIfmlWv39/UybNi01TVuqMTQ0xP7778/IyAjPPvvsrkzpU089xaxZs2JJhKotgVWJoohkyH8AryMYYRd3/62ZvSbZkKqnVip7qld3l1IqTfPqMRiRvmcRqUSUGtLzgB+b2TZN+xKPZhi6e9asWXR3d7Pvvvvy9NNPs337dp5++ml27tzJjBkzYhn6XiWwItIK3H1DwaLnEwmkDsrV2A0ODtLf38+SJUvo7+/PVCuhahV2d5kwYQLDw8Psvffeu7q7NDqeekwTo9ZIIlKJkjWkZraXu+9w97ZGBtSKmmGQo1xp6EknncQDDzzAmjVr2LFjB2984xs5//zzY0mEVAIrIi1gg5n9OeBmNhn4MGHz3awqVmOXtlFZG6Wwu0vO5s2bmThxYsMLpus5GJFaI4lIVOVqSO8ws++b2blmdkijAmpFSU2oXU+50tDDDjuM4447jo985CP8+Mc/5uKLL44tQVIJrIi0gHMJWirNAjYCx4TPm0o1YwI0g1x3l0mTJvHss88CMGnSJLZs2cLee+/d8ILpZmixJSLZU7KG1N3nmNnBwFnAl8xsFvAz4AbgNnffXmpbqUyzDN2dRGmoSmBFpJm5+x+ABUnHEbc0jsraCLNmzWL79u1s3LiRTZs24e7s2LEj1u4u48WT9RZbIpI9ZQc1cvd1wGXAZWY2CTgVeD3wL2b2pLu/sQExtgRlrEREpJCZXQlc4O6j4fPpwBfc/QOJBlZnrZoRSqK7S5R4QF1hRKRxooyyC4C7Pwf8JHwQ1piKJCrKaID1GDFQRCQhR+cyowDuvtnMjk0wnli0akYov4XUlClTeNOb3pRoGtUsLbZEJFvM3cuvYHYvULjSU8Aq4F/c/Y8xxVaTOXPm+KpVq5IOQ2KUPwhG/g1Mfj/SKOuISGsxs7vcfU7ScURhZr8FTnf3zeHzToJuM69IIp4409Zc4eHw8DBdXV0qPBQRyZhq09coNaQ3EAwxf1X4/J2AEWRKvwH8RaUfKlIPUUYDrOeIgZIeqvWWFvIF4Bdm9t3w+duAZQnGExt1XRERaU1RMqQnu/vJec/vNbOfu/vJZvbXcQUmMp4og2BkdaAMZbhKa9XpIaQ1ufs3zewuYC5BYfBfuvsDCYclIiJSN+WmfcnZ18xOzD0xsxOAfcOnO8bb2MwmmtndZvaj8Hmnmd1sZoPh3+l5637SzNaY2cNm9rq85ceb2b3haxebmUXeQ2lauUEw8hUOghFlnbSp18TkzapVp4eQlvYQ8D3gB8CfzGx2wvGIiIjUTZQa0g8CV5hZLhM6BnzQzPYB/jXC9hcQTOKdm9jqE8BKd/+cmX0ifP5xM3sZQXPgo4AuYIWZHeHuzwP9wCLgDuB6gpF+b4iyg9K8ogyCEddAGXHWYKqZcXlZrfUWqYaZnQ98BthE0H3GCMZ1ODrJuKS5VZLGqUWPiNQqSg3p6nDwhGOAY939aGCNuz/t7teU29DMuoE3Al/LW3w2cGX4/5XAW/KWX+3u2939UWANcIKZHQi0u/svPRiB6Zt520gGDQ4O0t/fz5IlS+jv76+65i83GmBbWxtDQ0O0tbXt0WwzyjrVxB9nDWaaJiav13dVT1ms9RapwQXAS9z9KHc/2t1fEabDIrGoJI1Tix4RqYcoNaTfM7Oz3f0pADM7APg/4PgI234J+BjQlrdsprs/DuDuj5vZ/uHyWQQ1oDkbw2XPhf8XLpcGq0cpaL37/0UZBKPWgTIK93vTpk2x1mCmZT6+RvTVLHVOlTvXWnV6CGlZGwgGERRpiEpa6ahFj8RFNe+tJUoN6feB74Z9QQ8BbgI+Od5GZvYm4PfuflfEWIr1C/Uyy4t95iIzW2Vmq5588smIHytR1KsUNGv9/4rt90033cS2bdt2W6+eNZi9vb2MjIwwOjrKzp07GR0dZWRkhN7e3rq8f1Rxf1elzqmbb7657LlWrNZ73rx5rFixIlU1uSJ18ghwazjGwt/nHkkHJc2rklY6aWrRE7c0thhqVqp5bz3j1pC6+3+Z2WSCjOkhwN+6+y8ivPfJwJvN7A3AFKDdzP4b2GRmB4a1owcCvw/X3wgclLd9NzAcLu8usrxYrJcDl0MwV1qEGDMnqRKjq666iocffpjt27czffp0jjzyyF2Zk0o+P2v9/4qV/s6cOZN77rmHAw88cNd69azBTMvE5HF/V6VK1q+44gqOPfbYsiXu+bXeGnVXmtz68DE5fIjEqpJWOmlp0RM3pTONpZr31lMyQ1pQAmsEmcV7gJPM7CR3/2K5N3b3TxLWpJrZ6cCF7v7XZvbvwHuBz4V/fxBuch1wlZl9kWBQox7g1+7+vJmNmdlJwK+A9wCXVLifTSGpC+Lg4CA33ngjBxxwANOnT+eZZ57h5z//Oa9+9asZGxur6L2ylngVy5Qdc8wx3HTTTYyOjsbWZDQN8/HF/V2VyvBu2LCB0047bY/lpTLCuYRr+/bt3H777YyOjjJ58mSWL1/ORRddVDYGNQmStHP3zyYdg7SWSrpFNLoLRVLXbGWQGitrlRdSu3JNdtvyHvsC1xIMNJRbVq3PAWeY2SBwRvgcd78fuAZ4APgxcF44wi5AH8HASGuAtbToCLtJNXddsWIFM2fOBMDMmDp1Kvvssw/33HNPxZmTtDRHjarYADpTp07lzDPPrOtASXGrpqlRpd9VpZ9RanCigw46qKJBi4aGhti2bRs///nP2bZt264bhZtuuqlsDEk1CVKzL6mEmc0ws383s+vN7Ce5R9JxSbLivI5UMhhgHAMHlpJkM85WapqcBhq8sPWUrCGtZ6msu98K3Br+/0dgXon1lgHLiixfBby8XvFkVVIlRkNDQxxzzDH88pe/BIIM2c6dO9m0aVPFGcm0NEeNqlTpb9ozoPmqrVmv5Luq5jNKHdsPfOADrFy5co/lpUrcZ82axU033cS0adOYNm0aABMmTGDmzJllS6+TKPFWsy+pwnLgO8CbgHMJWhZpkIQW1ojrSCWtdBrVoifJWsqste7KeusfDV7Yeso12b0cuNjd7yvy2j7AO4Dt7r48xvgyr54XhaQuiLNmzWJsbIyTTz6Zhx56aFeTyDPPPDO2kXHTImsZ6GJqScSjflfVfEa5Y3vIIYdEPua9vb1861vf4oADDsDdeeaZZ3j66ad59atfXbb0OokCHjX7kiq8yN0HzOwCd78NuM3Mbks6KClPc1XXptjxS7IZZ5YySEkXfI537kf5bZS6PwDo7+/PbEZbSis3qNGlwKfN7BXAfQQlslMI+na2A1cQlNxKCfW+KCR1Qcx9bmdnJ6eeeuquz12wYEGsn5sW9chAV3NzUq8bmkYk4tV+RqljW2np/Ote9zruueceRkdH6ejo4JhjjmHvvfemra1074IkCnjq9V1kvfRbKvJc+PdxM3sjwaB+3WXWl4TFnSFo9v51pY7flClTEqulzFLhdJIFFuOd+5X8NgrvA5LOaEu8yjXZvQd4u5ntC8wBDgS2AQ+6+8ONCS/b6n1RSOqC2MjPbcYb7WouovW88DYi41XsM9auXcvGjRtZsmRJ7N/lu971LrZt20ZnZ2fkwpqoBTxpa+WgRLnl/IuZ7Qf8A8GAfu3AR5MNScqp16j0pWSt+WilSt07bdu2jZGRESCZWsqstO5KssBivPveWu6LW6FlQCuLMu3Lnwj7f0pl4rgoJHVBbMTnxnWjnXQmt5qLaKXblNvHRtSsF37G2rVruf322zn11FMbkmmqptAkyjZpbOWgRLm1uPuPwn+fAuYmGUvaJH1tLxVTvUalLyVLzUerUereaWxsLDO1lElKssBivPveWu6L87fdtGkTDz30EJs3b8bdq/rtp/H60crGzZBK9Zq9FLPe4rjRTkNtUjUX4Eq2ibKPU6dO5ZZbbgHgxBNPrPv+F2buNm7cyKmnnrrrMxqZaXKPPgXxeAUtaWzl0OzN9SRgZpcAJU9md/9wA8NJnTRc24spNio9wD333MOZZ55Zl8/IUvPRapS7d8pKLWUtas0oJVlgMd59by33xbltt2/fzs9//nOmTZvGlClTcPeKf/tpvH60ega53LQvUqOsTXGStDiGVU9qqpx81QxfXsk25fYxd9GdOnUqZ599NnPnzuWZZ56pz44V6Onpoa+vj6VLl3L44Ydz+OGH7/Z6nEPkxzUdQBznZP5x6uvrq7oJdj4VdDWlVcBdBGM3HAcMho9jgOdLb9Ya0nBtLyY3Kv3WrVvZunUr7l71qPTl1HodSbNWvneqR1pW61Q8tUwpNN53V8t3m9v2N7/5DVOnTsXM2Lp1K8cdd1zFv/20XT+SnNIoLSJnSMORdaUCjZyfqxnEcaOdhrnDqrkAV7JNuX1M6qLb6ExTXPuZxsxfK9+stRJ3v9LdryQYSHCuu1/i7pcQTJt2TKLBpUAaru3FzJo1i6lTp3LyySczdepURkdHAaoelb4VtfK9U73SsmoLLGrNGI333dXy3ea2feaZZ9i+fTtTpkzh5JNPZubMmRX/9tN2/UhbBjkJ4zbZNbM/B74G7AvMNrNXAn/r7h+KO7hm0ArNS+oljmYmaWg2HVf/xpxy+5hU885GNxmKaz/T2Fer2ZvryR66gDZgJHy+b7gstRrR9CwN1/ZiWn1U+npp1XunpLtk1KObynjfXS3fbU9PD29+85sZGxur6beftutH0t97GkTpQ/ofwOuA6wDc/bdm9ppYo5KWFMeNdq0ZinrdWFVzAY66Tbl9XLFiRSIX3UZnmuJKXNKa+WvVm7UW9TngbjO7JXx+GnBRcuGU16i+WWksLIL0XjOkful5nAUuSWeUspAxqsdvP23Xj7i/9yz0T7XxBgAxs1+5+4lmdre7Hxsu+627v7IhEVZpzpw5vmrVqqTDkBTI/RCHh4fp6uqK/EPMv7HKv2CNN11LEj/6UvtYzT5kUavsp9SHmd3l7nOSjiMqMzsAODF8+it3fyKpWMZLW/v7+/eovRgdHaWtrY2+vr66xlLttb1VZOEmtFHqlUbEndYknZY18vdbi3r89tN0/Yjze2/0OVVt+holQ/pd4IvAV4CTgA8Dc9z9ndUE2ijKkEqtKr0wJ52QlJKmi26cWmU/pXZZypCamQELgMPc/Z/NbDZwgLv/Ool4xktblyxZQnd3NxMmvDBExc6dOxkaGmLp0qWNCFFobHqUhYxvvTJajciwJZmWlTpv5s2bx5o1a1L9HcepEed4XN97owsZqk1fozTZPRf4MjAL2AjcBJxX6QeJZE2lTVdq7XsR1wUvq807Kz0eWd1PkXFcCuwEXgv8MzAG/C/wqiSDKiXpJocSaNR8xWmcPqOYejVFreV9oqZpSaZlxZqcH3vssaxcuTL133FcGnWOx/W9Z6EZNkTIkLr7HwhKZ0USU+/MWpT3q/TGqtaEKguJej2V+w5a8XiIlHCiux9nZncDuPtmM5ucdFClpK1vVqtq1E1oozK+tapXQUm17xM1TUtDbXNhxqi/vz9133Ejj1NWzvFSslJIOO60L2Z2pZl15D2fbmZXxBqVSJ56z88U9f0qnV6jlilCWm3I7/G+g1Y7HiJlPGdmEwEHMLMZBDWmqdTKU3akSaOmrErb9Bml1Gu6rGrfJ0qalta5KNP2HTf6OKVt/yuVlaniojTZPdrdR3NPwtLZY+MLSWR39S6divp+lY6WWEvNQJqaVDSi5HG87yBNx6Na+cdxr732wsx47rnnWrL/jdTkYuBaYKaZLQPeCixJNqTy1Hw+eY2qqc5K7Uu9Rj+u9n2ipGlprYlL23fc6OOUtv2vVFZG/o6SIZ1gZtPdfTOAmXVG3E6kLspdyKvJPFWS2ankxqqWH31aLniNaio73ndQ7HisXbuWjRs3smTJktRn6vKP46RJk7jllmDGjrlz5+4qzVWtkUTh7svN7C5gXrjoLe7+YJIxSfo16iY0S02061VQUs37REnj01oQm7bvuNHHKW37X40sFBJGyVh+AfhFONouwNuAZfGFJLK7UhfyiRMnVpV5ipIwVFtLWO2PPi0XvEaVPI73HRQej7Vr13L77bdz6qmnZqJPaf5x/O1vf0tnZydmxu9+9ztOO+20XeukMXZJpWlArtnu1IRjkYxoxE1ovTO+aehDGYcoaXxaCqYLpa2GrdHHKW3736yiDGr0zbB0di5gwF+6+wOxRyYSKnUhnzp1alWZp/EShiQG1EnLBa9RJY/jfQeFx2Pjxo2ceuqpu45HWpoylZJ/HEdHR3fFOzo6CqSj1Fuywcw+TVAQ/L8EafDXzex/3P1fko1MJFCvjG890t60ZmijpPFpKZguJk01bEkcpzTtf7OK2vT2IWBzbn0zm+3u62OLSspK6wU3LqUu5FdeeSUzZ87cbd0oN/rjJQxJ9eNIwwUv7pLH/HN3ypQpbNu2jbGxsaKJc/7xyM1tmC/Nmbr849jR0cG2bdsws13HNQ2l3pIZ5wDHuvszAGb2OeA3gDKk0lTqMXVamkdnHy+NT0vBdNrpODWncTOkZnY+8BlgE/A8QQmtA0fHG5oUk/YLblyKXchryTyVSxjS2o+jnHoVUsRZ8ljs3I06UXtamzKVkn8cjzjiiN36kOZGuEtDqbdkwmPAFOCZ8PnewNrEohGJSa1pb1oHBapEGgqms0DHqfmMO+0LcAHwEnc/yt2PdvdXuLsyownRdBgviGso60YNl18v9RwCPc4pG2o5d7MybHlO/nHcsWMHp59+OnPnzmXHjh2aBkMqtR2438y+YWZfB+4D/mRmF5vZxeU2NLPXm9nDZrbGzD5RZr1XmdnzZvbWOscuElmtaW8c03MMDg7S39/PkiVL6O/vT3wKlmah4yqFojTZ3QA8FXcgEk0Wa++g8hq8KOvH1Wwjzf04iql3qXBcJY+1nLtZbKKjElypk2vDR86tUTYK5y79KnAGsBG408yuKxwDIlzv34Ab6xKtZEbc3X8qff9a0956t6Rp1RZptYjyneu4SjFRMqSPALea2f8RlNQC4O5fjC0qKanSC24a+ptWevGpZP38fp9DQ0O7attq2cesZX6yUkhR681CpRm8NJz7IrVy9yur3PQEYI27PwJgZlcDZwOFgxKeTzBg0quqDlIyJ+5MQTXvX2vaW+/C5GZoAtxIUb9zHVcpJkqGdH34mBw+JEGVXHDTUgpV6cWnkvXj2scs1W6Nl9FLS8askTXPaTn3q5WW70ySZ2ZvApYCBxOk2Qa4u7eX3RBmEbRwytkInFjw3rOA+cBrUYa0pcSdKaj2/WtJe+tdmJyVwl5IR5oR9TvP0nGtt8Lv6cUvfjFr1qxRWk+EPqTu/tlij0YEJ3uqpI9fWvqbVtqvo5L107KPOUn0iyjXv7Ke/UtrFWf/1EJpOy8qkabvTFLhS8B7gRe5e7u7t0XIjEKQcS3kRd774+7+fNk3MltkZqvMbNWTTz4ZJWZJuTj6Wzby/Uvp6emhr6+PpUuX0tfXV1P6kpXxJNKSZkT9zrNyXOut8Ht65JFH+NSnPsUjjzyitJ5oo+zOAD4GHEUw0h8A7v7aGOOSMqKWIKalFKrSppqVrF9uH+MsMSz23kAitXLlSoX7+/tT1TSmUTXPaTn3q6HmTFJgA3CfuxdmJsezETgo73k3UJgbmANcbWYAfwa8wcx2uPv381dy98uBywHmzJlTaRySQnG3rMli96JCWRlPIi1pRtTvvNxxTeN5UC+F39Pw8DCdnZ0MDw/T09PT8ml9lFF2lxPMQ3oo8FmCIejvjDEmqZO0lEJVOkJqJeuX2se99torthLDUqWRy5cvT6xWrlSpcFKl1ElLy7lfjVb9zqSkjwHXm9knzezvc48I290J9JjZoWY2GXgncF3+Cu5+qLsf4u6HAN8FPlSYGZXmFHfLmkrS8bTU8BVqZKueWqQlzYj6nZc6rkAqz4N6KfyeRkdH6ejoYHR0dNeyVk7ro/QhfZG7D5jZBe5+G3Cbmd0Wd2BSu7SU7lXar6OS9Uvt45QpU2IrMSxVGnnLLbdw9tln77Zu0rVyxUos165dy8aNG1myZEnTlUDmRDn301oSm7U5VyV2y4A/EbRQijyOg7vvMLO/Ixg9dyJwhbvfb2bnhq9fFkewkg1xt6ypJB1PSw1fMVkYTyItaUYl33mx45q2Fl31Vvg9dXR0sHnzZqZPn75rnVZO66NkSJ8L/z5uZm8kaPLTXWZ9SYk0jRZb6UU96vql9vHKK68sWmJYj8xhqeagZpaKRCFfYcZs7dq13H777Zx66qmZHOwnqvHO/TQPepSWgiRJjU53P7OaDd39euD6gmVFM6Lu/r5qPiNpaS1YyoJS6Wy9ujxkrXtRViWZZhT7/fX19VX1Xs1+HhR+T11dXaxdu5ajjjqKnTt3tnxaHyVD+i9mth/wD8AlQDvw0VijkrrJQulerYrtY5wlhqXe+8QTT2RkZARIT0aiMGO2ceNGTj311F3Hq9lKIPOVO/fTXiKfloIkSYUVZnamu9+UdCBpk+aCpSxrdI1bWmr44hRnwUlSaUa9f3/Nfh4Ufk+HHXYYZ5555q5Rdls9rR83Q+ruPwr/fQqYG284IvURZ4lhqffO9YFIW0YiP2O2ZMmSpi6BjCrtJbGtUJAkkZ0HfMzMthO0WIo67UvTS3PBUpY1usat2VuFNKLgJIk0o96/v2Y/D6D493TGGWckFE26lMyQmtkl7DlE/C7u/uFYIhKpUmEJ5Lx582IpeRqvNDL/M3LTwKSlOVmzl0BGlcXjoKaJrcnd25KOIa3SXrBUT7X8/ivdttE1bs3eKqRZC07q/ftr9vNAyitXQ7oq/Hsy8DLgO+HztwF3xRlUlummMRnFSiBXrlwZW9OtKKWRaWxO1golkFFk7Tik8VySeJnZke7+kJkdV+x1d/9No2NKm1oKlrKUVtfy+69220bXuDVzq5BmLTiJo2C3mc8DKa/ktC/ufqW7Xwn0AHPd/RJ3vwSYBxzToPgyJa1Dl7eC/BLIRk+5kqWYsjKMfdyydhzSeC5J7HJTu3yhyOPzSQWVJpVOKZaTtbS6lt9/rdeOXCufJUuW0N/fn9pjlGZZnoasnGp/f3HRuZptUQY16gLagJHw+b7hMinQrM0ysiCNJZCNiqma5lg6H7N1HNJ4fku83H1R+FdjN5RQbRO/rKXVtfz+a9lWLTPqI2qLnCzV2kO6mti24rmatfNlPFEypJ8D7jazW8LnpwEXxRZRhummMTlp7BPYiJha8SKc02wX43LSeH6LpEE1BUtZS6tr+f3Xsm3WMu6QznQhSsYtq2l5Wgp2s3iu1iKr50s5JZvs5rj714ETgWvDx6vDprxSoFmbZWRB2pqONCqmVm3KmbUmd7VK4/ktklVZS6tr+f3Xsu3Q0FDR+byHh4er3pc4pTld6Onpoa+vj6VLl9LX17dHpqFV0/J6ydq5WqtmPF/GzZCamQG9wCvd/QfAZDM7IfbIMkg3jclJY5/AcjHVq69Dq12Ec5rxYlxOGs9vkazKWlpdy++/lm3Hy7gn1Wev1OdmOV1o1bS8XrJWyFSrZjxfojTZvRTYCbwW+GdgDPhf4FUxxpVJaWpP34rS0nQkX7GY6tnUolRzrL322itV083UW9aa3NVDGs9viZ+ZzQd+4u5Phc87gNPd/ftJxpVlWUyra/n9V7ttub6PSTUZLPe5WU4X1C2jNlkbOb9WzXi+RMmQnujux5nZ3QDuvtnMJsccV2bpprG1Rem/Us++DsUuwmvXrsXdmTp1atP0LSg0a9Ys1q5dy/DwMKOjo3R0dNDV1cVhhx2WdGgi9fYZd78298TdR83sM8D3kwsp+5RWj69cxr2/vz+RPnvl0s8s36SPl/lPW7/YtIlayNQsx7IZM+BRMqTPmdlEwAHMbAZBjamI5IlaYlzPUtxiF+EDDzyQadOmjXujEOeFOY73zn/PLVu2cNtttzFr1iw6OjrYvHkza9eu5cwzz6xL/CIpUqxrTZS0W6RmpTLuSdVGlvvc97znPZm9SS+VoQKabvCauIxXyNRMAwFlsZXHeKIkahcTDGY008yWAW8FlsQalUgGRa35rHcpbuFFeMmSJUX7FuTfKMR5YY7jvQvf88Ybb2TKlClMnDiRp556iunTp3PUUUexZs0azjjjjJriF0mZVWb2ReCrBAXD5wN3JRuStLqkaiPLfW7Wb9KLZaiSqoluRs02Em+ztfIYN0Pq7svN7C5gXrjoLe7+YLxhiWRP1BLjuJtaRLlRKHdhzv0tVrtZS5Pkq666iv3337+qWtPC99y+fTtdXV1MmzaN0047DYCdO3dmoq+QSIXOBz4FfAcw4CbgvEQjkpaXVJPB8T632W7Ss9wvNm10LNMtarOfaUCu2e7U+MIRya6oJcZxl+JGuVEodWFevXo169atK1q7CaWbDsELmdi77rqLk046abfjsG3bNm666SbOPvvsqmpNC+OdPn0627ZtY3R0dNeyrPQVEqmEuz8NfCLpOKS1FSuMTKI2spr0M8v9BrPcLzZtWuVYZvV8N3cvv4LZp4G3EYysa8BbgP9x93+JPboazJkzx1etWpV0GFKjLP2w8puV5mcEk+ifkDtuw8PDdHV17XHc+vv7GRsb2+3CPDo6yt13382xxx67x/K2tjaAotts27aNbdu27drvG2+8kc2bNzNv3jxmzpwJwA033ADAWWedtcf79vX1jbs/hfFu2rSJlStXMn36dF73utcleqwle8zsLnefk3Qc5ZjZl9z9I2b2Q8IxHPK5+5sTCEtpawtKU9pWqSzHDtmPP01a4VimYR+rTV+jZEgfBI5192fC51OB37j7S6uKtEGUaGZfGn5YlRovI5gWpY7tli1bOProo5kw4YVxVHJNYd2d7u7uPV677rrrOP3003fLLP7oRz/imWeeoauriylTpvDYY4/xlre8hQMPPHCP9126dGlV8a5du5YDDzyQ559/PhXHOkuFJ60uIxnS4939LjM7rdjr7n5bo2OCPdNWnffNr1QBZtQCxSRlOfacrNxXZEGzH8s0nO/Vpq9Rmuw+BkwBngmf7w2srfSDRCqVxQ7oWem/UqrZ04oVK8o2aSn2mrvvMYjS5MmTGR0dxcwA2GefffjTn/602zqVNJUpFu+FF16YmmPdTKP3STq4e27gomPc/cv5r5nZBUAiGdJ8Ou9bQ5b73mU59pys3FdkQbMfyyyf71EypNuB+83sZoJmQ2cAPzOziwHc/cMxxictLMs/rCwodWEu1/+02GsnnnjibhnVhx56iLa2NmbPnr1rwKHBwUFWr17NjBkzqh4AI80JSRYLTyQz3gt8uWDZ+4osazid960hy33v0hC7WhFIo6ThfK9WsfnNCl0L/BNwC3ArsBi4gWDYeQ09L7HJ/bDyZeWHlVW5msi2tjaGhoZoa2vbVdtR6rUFCxYwMjLC6OgoO3fu5IknnuD555/nyCOP3PW+hx9+OIcffnjR920GQ0NDRafaGR4eTigiyTozOyfsP3qomV2X97gV+GPC4QE671tFb2/vbtf40dFRRkZG6O3tTTq0cSUde64VwdjYGN3d3YyNjTEwMMDg4GBDPl9aS9Lney2iTPtyZTVvbGZTgJ8SNPHdC/iuu3/GzDoJhq8/hKA58NvdfXO4zSeBhcDzwIfd/cZw+fHANwhG+L0euMDH6/wqmRf3sPLNUmpZ7/0oVxNZ6rX85rT7778/3d3duwY0gqAg4RWveEVm+uzki3J8s1wqKan1C+Bx4M+AL+QtHwNWJxJRAZ33rSHL83smHbtaEdSmWe7TGiXp870WUQY1ehOwFDiYIGNpgLt7+zjbGbCPu//JzCYBPwMuAP4SGHH3z5nZJ4Dp7v5xM3sZ8G3gBKALWAEc4e7Pm9mvw23vIMiQXuzuN5T7fA1q1Bzi6oCexQGTiknjftQ7piQTpKj7ksbvQUrLwqBGOWa2D7DN3Xea2RHAkcAN7v5cEvHkp60676UZxJnGLFmypOhggFEH9Gtlur5kU7Xpa5Qmu18i6MPyIndvd/e28TKjEORY3T03ismk8OHA2UCu1vVKgmlkCJdf7e7b3f1RYA1wgpkdCLS7+y/DWtFv5m0jTa6np4e+vj6WLl1KX19f3S5C+aWWEyZMoKOjg87OTlasWFGX92+UNO5HuWa/lUq6uVPU41vPfRYp8FNgipnNAlYC7ydoMZQ4nfeSdXGnMep6VL003t9IfKIMarQBuK+aJrJmNpGgn+mLga+6+6/MbKa7Pw7g7o+b2f7h6rMIakBzNobLngv/L1xe7PMWAYsAZs+eXWm40kKaZcCktO5HvQYgSrq5UyXHN82DLkmmmbtvNbOFwCXu/v/M7O6kg8rReS9ZFncaM17XIzVJLS2t9zcSjygZ0o8B15vZbQQj7gLg7l8cb0N3fx44xsw6gGvN7OVlVrdib1FmebHPuxy4HIJmRePFJ62rWfo+Nct+lJJ0gtTsx1cywczs1cACgjEWIFraLdJyKs3gxZ3GlOvTp2mTyps1axZr165leHiY0dFROjo66Orq4rDDDks6NIlBlERtGfAngrlIJ1fzIe4+Go4M+Hpgk5kdGNaOHgj8PlxtI3BQ3mbdwHC4vLvIchGguhLGuAdMapRm2Y9Sks4QNvvxlUz4CPBJ4Fp3v9/MDiMY9V5E8lSTwWtEGlOqFUGltbNJ1aY26nMLP2fKlCncfvvtu47R5s2bWbt2LWeeeWbdP1uSF6UPaae7/6W7f8bdP5t7jLeRmc0Ia0Yxs6lAL/AQcB1Bn1TCvz8I/78OeKeZ7W1mhwI9wK/D5r1jZnZSOFDSe/K2kRZXbf+PZun71Cz7UUrSQ5g3+/GV9HP329z9ze7+b+HzRzT/t8iequlzmGQaU8m0SUmNp9Cozy32OVdffTVHHXUU06dP56mnnmL69OmceuqprFmzpq6fLekQpYZ0hZmd6e43VfjeBwJXhv1IJwDXuPuPzOyXwDVhf5j1wNsAwpLfa4AHgB3AeWGTX4A+Xpj25YbwIVJT/49m6fvULPtRTBqGMG/m4yvpZ2a3UKSbiru/NoFwRFKrmua3SaYxldTO1quva6W1neU+N/e3HjWnxT7nueeeY+vWrcydO3fXerkRiqX5RMmQngd8zMy2EwwwFGnaF3dfDRxbZPkfgXkltllG0ES4cPkqoFz/U2lRSfcxlPorlmBmcf7SNNHAGZl2Yd7/U4C/Iii0FZE81Ta/TarQsZIuIVHudca7zlfTpLnU5957772sW7eubv1fi33OAQccwKZNm3ZbpjEcmte4TXbDaV4muPvUSqZ9EWkEDan+gsHBQfr7+1myZAn9/f0NmxqlnpKe5qUZ6Zhmm7vflff4ubv/PXBi0nGJpE3SXTwqVUmXkPHudaJc58dr0lzsHqLU546MjNR1SpZinzNr1iz22muvzHyfUpuSNaRmdqS7P2RmxxV73d1/E19YItE046Az1dRmNctofUlP81KtNNdAZvWYSsDMOvOeTgCOBw5IKBzJsDRfp+ohDV08KhW1dna8e50o1/lytayl7iHmzZvHypUr9/jcjo6Oov1fq22dVmz/Jk6cyIUXXsiaNWsy831K9co12f17gjk9v1DkNQfUf0USl8UEqJxqM5bNkunIYhPstBcGZPGYym7u4oUp0HYAj/LC9C8ikaT9OlUvzdrnf7x7nSjX+XJNmkvdQ6xZs4Z58+ZxxRVXsGHDBg466CA+8IEPsGbNmrqOTlxu/84444yq3jNpzV4AVG8lM6Tuvij8O7fUOiJp0EwJULUZy2bJdCQ9zUs10l4YkMVjKi9w90OTjkGyL+3XKRlfuXudKNf5crWsV155ZdF7iNWrV7Nu3TqOPfZYTjvtNLZs2cLKlStL1pzW0jqtme7lWqUAqJ6iTPsiIg1SyTDw+ZqlL229+gA1sj9ttd9Zo2StX5XszswmmdmHzey74ePvzGxS0nFJtqT9OiW1iXKdL9dntdQ9xObNm4v2FV2zZo2mRCujmimIWl2UUXZFMierTSUqrc3K7efq1at55JFHOProozn88MMz25e2Hk2wG10ymfYayGZr1t6C+oFJwKXh83eHyz6YWESSOWm/Tkltol7nS9VClqo9nT59esm+os1Uo1lvzdJqrZGUIZWmk+WmEpUM0pS/n6985SvZd999Wb16NVu3buXlL395ZjMdtSZyjW6aloWBtXTjkGmvcvdX5j3/iZn9NrFoJJOycJ2S2tRynS+VoV2xYoUKMqi8kkMFQJUbN0NqZvOBn7j7U+HzDuB0d/9+vKGJVCfLfWUqqc0q3M+enh5mzJhBW1tbS8/b2eiSSdVASsyeN7PD3X0tgJkdBjyfcEySMUlep7LaYqlesrL/pTK0rV6QUU0lhwqAKhelhvQz7n5t7om7j5rZZ4DvxxaVSA2y3lQiailn1vczLkmUTKoGUmL0j8AtZvYIwUi7BwPvTzYkyaIkrlNZbrFUD1nf/7gLMrKQWa+mkkMF1ZWLkiEtNvCRmvpKarVKU4ms7mfcCVC9SiaTSiizkEBL47j7SjPrAV5CkCF9yN23JxyWSCRZbrFUD82w/3EVZGQls15t4b8KqisTZZTdVWb2RTM73MwOM7P/IJgXTSSVWmVU0SzuZy4BGhsbo7u7m7GxMQYGBuo6Cm65kQTTFGfaPrdRoxJLZczsPGCqu692998C08zsQ0nHJRJFq4/u2+r7X05WRqJtllkM0i5KhvR84FngO8D/AM8A58UZlEgt6pEhyYIs7mejEqCenh76+vpYunQpfX19FR+TpBLKJD43qUywRPY37j6ae+Lum4G/SS4ckeha/Wa+1fe/nKxk1rNY+J9F4za9dfengU80IBaRummVphJZ28+s9HtNKs4kPrcZmpQ1uQlmZu7uAGY2EZiccEwikbT64C6tvv/lZKXbkfqDNkbJDKmZfcndP2JmPwS88HV3f3OskYlI08lKAlTPOCvpE5rE8clKIUELuxG4xswuI0iLzwV+nGxI0grq0Z+91W/mW33/y8lSZj1rhf9ZVK6G9Fvh3883IhARaX5ZSYDqOTBSJYM2JHF8slJI0MI+DiwC+ggGNboJ+K9EI5KmV88BZ1r9Zr7V978UZdYln4WtgEqvYHaBu395vGVpM2fOHF+1alXSYYhIgVyp+/DwMF1dXakdRbYecfb39zM2NrZbZm90dLTsXLGNPj75N575meC090euhZnd5e5zko6jGmZ2CnCOuycyloPS1tZQzbVLRKTa9DXK9C3vBQozn+8rskykKWkajvrKSmlxPeKspjlso4+PSqnTz8yOAc4B3gE8Cnwv0YCk6akpv4g0Urk+pOcA7wIONbPr8l5qB/4Yd2AiaZCVebLSLGsZ+nrGW6/msHEfw6wUErQSMzsCeCdBRvSPBCPdm7vPTTQwaQlqyi9xyNr9QKtr5PdVssmumR0MHAr8K7uPsjsGrHb3HbFEVCdqViT1oGZLtYnSHDRNCVS9m6/W4/1asUlt3LLQZNfMdgK3AwvdfU247BF3PyzJuJS2tgZdd3ZXj3QqTWldElrlnGqW77na76va9LXkPKTuvs7dbwV6gdvd/TbgcaCbYGAFkaZXbJ6sbdu2cd1117FkyRL6+/s1X2MZ482rmbY5MOs9D2g95orNyuThUnd/BTwB3GJm/2Vm81DaKw2SxXmu41KPdCptaV0SWiEta6bvudHfV5Q+pD8FTjWz6cBKYBVBP5YFsUQkkiKFzZY2bdrELbfcwvTp09WEN4Lx+iGlbQ7MOPpN1docVn25WpO7Xwtca2b7AG8BPgrMNLN+4Fp3v2m89zCz1xOM9zAR+Jq7f67g9QUEo/gC/Anoc/ff1m8vJMvUlD9Qj3QqbWldElohLWum77nR31fJGtI85u5bgb8ELnH3+cDLYolGJGV6e3sZGRlhdHSUnTt38pvf/AaA4447rmlL+Oopl6HPl98PqVgNdHt7O8PDww2LMd948SYhjTFJ47j70+6+3N3fRNBC6R5270ZTlJlNBL4KnEWQZp9jZoVp96PAae5+NLAUuLyesYs0g3qkU2lL65LQCmlZM33Pjf6+ImVIzezVBDWi/xcui1KzKpJ5hc2WnnnmGebOncvMmTN3rZPVi00jFGboR0dHGRkZobe3F0hfAjVevIpJkuTuI+7+n+7+2girnwCscfdH3P1Z4Grg7IL3+4W7bw6f3kGQ4RWRPPVIp9KW1iUhzrRscHCQ/v7+xLtSNdP33Oh7jygZ0o8AnyRoInS/mR0G3BJLNCIp1NPTQ19fH0uXLuXNb34zU6dO3e31rF5sGmG8fkhpy2ylsd9UGmOSTJgFbMh7vjFcVspC4IZiL5jZIjNbZWarnnzyyTqGKFmVlgxAI9SSTuWO0+rVq1m5ciWDg4OpSOuSEFdalqZ+m2m7p6lFo+89So6ym3UaCVDikIVR4rI2wlsu3uHhYbq6ulIfr2RfFkbZrZWZvQ14nbt/MHz+buAEdz+/yLpzgUuBU9y97LRuSlslC+lgvVWTThUep7Vr17J69Wpe/OIX8/KXv1xpXZ2kbTaEVr+nqTZ9HbfprZndAuyRa43YZEikqeRKjHIZvq6uLubPn5+ai03a50399vLlLFu8mAfXr+els2ezeNkyzlmwIBWxiTSZjcBBec+7gT36FpjZ0cDXgLPGy4yKQHMN3BJVNQM8FR6nnp4eZsyYoWnj6ixtgyVpMLDqROkLemHe/1MIhqJP9RykInFK88UmzTcK316+nMWLFjGwdSunAD9bt46FixYBcM4CDdotUmd3Aj1mdigwBLwTeFf+CmY2G/ge8G53/13jQ5QsSlsGIK10nBqjcDYEUFeqLBo3Q+rudxUs+rmZ3RZTPCJSgzQngMsWL2Zg61bmhs/nAgNbt3L+4sWZyJBmrSm0tDZ332FmfwfcSDDtyxXhOBDnhq9fBnwaeBFwqZkB7Gj2psxSO2UAotFxaoze3l4GBgYAdmtCPn/+/IQjk0pEabLbmfd0AnA8cEBsEYlI1dKcAD64fj2nFCw7JVyedmlvCi1SjLtfD1xfsOyyvP8/CHyw0XFJtikDEE2rHKekC2vj6kqV9H6Vk+bYqhVllN27gFXh318C/0AwGp+IpEyaR3h76ezZ/Kxg2c/C5WmX3xQ6a/PPLr9qOYcccQgTJk7gkCMOYflVy5MOSUQyTCN/R9MKxyktI9zmz4bQ19fXVCP3ZiW23IjSQFU1IFGa7B5azRuLSOOledClxcuWsTC/DymwcNo0li1blnRo40pzU+hyll+1nEUfXcTWs7bCO2Hd+nUs+mjQb3fBu9LfTFpE0inNYymkSbMfpzSPW1GLNO9XGmPLb0UGPFvNe0RpsjsJ6ANeEy66FfhPd3+umg8UkXilNQHM9RM9P2+U3WXhKLtpl+am0OUsvmhxkBnNFSseClvP2sriixYrQyoiIjXJamHteNK8X2mMrTCTXI0oTXb7CfqNXho+jg+XiYhU5JwFC7jvscd4fudO7nvssUxkRiHdTaHLWb92PRS2iJ4dLhcREalBrrA2XxYKa8eT5v1KY2xDQ0O0t7fX9B5RMqSvcvf3uvtPwsf7gVfV9KkiIhmS1b5Asw+fDYV5z/XhchERkRpktbB2PI3Yr1yfyyVLltDf3x+5D2gaj3mxTHKlomRInzezw3NPzOww4PmaPlVEJGPqPWhCIyy7aBnTbpgGjxJctR+FaTdMY9lF6e+3KyIi6ZbVwtrxxL1ftQxMlMZjnp9Jrta4fUiBfwRuMbNHAAMOBt5f9SeKiEhD5PqJLr5oMeu/tZ7Zh89m2X8sU/9RERGpi7SOW1GrOPer1oGJ0nbM8wfUBCZX8x5RRtldaWY9wEsIMqQPufv2aj5MREQaa8G7FigDKiKZ0ozzLIrkpHFgolrlMskf+tCHhqvZPsoou+cBy919dfh8upktdPdLq/lAEREREZFi8qeQ6O7uZsuWLQwMDCTeLFFao6CgEfuY1ZH74xSlD+nfuPto7om7bwb+JraIRERERKQl5TdnnDBhAh0dHXR2duaaA0pCKu33WO2gPUmqpW9nJeIcmCiLxx2iZUgnmJnlnpjZRKpsHywiIiIiUkqxKSTa29sZHq6qJaDUSSUFBY3K2NVbowpD4hqYKKvHHaINanQjcI2ZXQY4cC7w41ijEhEREZGWU645Yys0GU2rSvo91jpoT1Ia2bczjoGJsnrcIVoN6ceBlUAfcF74/z/GGZSIiIiItJ5SzRlf/OIXZ7b2pxkUm2uyVL/HrNZyV7KPaZTV4w4RMqTuvtPdL3P3t7r7XwH3A5fEH5qIiIiItJJSzRnXrFmjvqUJqqTfY1YzdnH27WyErB53iFZDipkdY2b/ZmaPAUuBh2KNSkRERERaUk9PD319fSxdupS+vj56enoyXfvTDCrp95jVjF1cfTsbJavHHcr0ITWzI4B3AucAfwS+A5i7z21QbCIiIiIimiojBaL2e8xl7HL9fbu6upg/f34mMnZx9O1slCwf93KDGj0E3A78hbuvATCzjzYkKhERERGRUG9vLwMDA0BQM7plyxZGRkaYP39+wpFVrhUGZ8pyxi7LsnrcyzXZ/SvgCeAWM/svM5sHWJn1RURERETqLuvNKXOyPDWHSFxK1pC6+7XAtWa2D/AW4KPATDPrB65195saE6KIiIiItLqs1v7ky/LUHMV8e/lyli1ezIPr1/PS2bNZvGwZ5yxYkHRYkjFRRtl92t2Xu/ubgG7gHuAT421nZgeZ2S1m9qCZ3W9mF4TLO83sZjMbDP9Oz9vmk2a2xsweNrPX5S0/3szuDV+72MxUUysiIiIimdJMgzN9e/lyFi9axCXr1vGMO5esW8fiRYv49vLlSYcmGRNplN0cdx9x9/9099dGWH0H8A/u/lLgJOA8M3sZQWZ2pbv3EMxp+gmA8LV3AkcBrwcuNbOJ4Xv1A4uAnvDx+kriFhERERFJWpan5ii0bPFiBrZuZS4wCZgLDGzdyrLFixOOLD6Dg4P09/ezZMkS+vv71dS6TirKkFbC3R9399+E/48BDwKzgLOBK8PVriRoDky4/Gp33+7ujwJrgBPM7ECg3d1/6e4OfDNvGxERERGRTMjy1ByFHly/nlMKlp0SLm9G6v8bn3Kj7NaNmR0CHAv8Cpjp7o9DkGk1s/3D1WYBd+RttjFc9lz4f+FyEZGyWmEkQxERyY4sT81R6KWzZ/OzdevInw/yZ+HyZtRs/X/TJLYa0hwz2xf4X+Aj7r6l3KpFlnmZ5cU+a5GZrTKzVU8++WTlwYpI01BJpoiIpFFPTw99fX0sXbqUvr6+zGZmFi9bxsJp07iFoPboFmDhtGksXrYs4cji0Uz9f6NYftVyDjniECZMnMAhRxzC8qvi6xsca4bUzCYRZEaXu/v3wsWbwma4hH9/Hy7fCByUt3k3MBwu7y6yfA/ufrm7z3H3OTNmzKjfjohI5uSXZE6YMIGOjg46OztZsWJF0qGJiIhk3jkLFrDs8ss5/+CDmWLG+QcfzLLLL2/aUXabqf/veJZftZxFH13Euj9fhy921v35OhZ9dFFsmdLYMqThSLgDwIPu/sW8l64D3hv+/17gB3nL32lme5vZoQSDF/06bN47ZmYnhe/5nrxtRESKarWSTBERkUY7Z8EC7nvsMZ7fuZP7HnusaTOj0Fz9f8ez+KLFbD1rKxwKTAQOha1nbWXxRfEMWBVnDenJwLuB15rZPeHjDcDngDPMbBA4I3yOu98PXAM8APwYOM/dnw/fqw/4GsFAR2uBG2KMW0SaQCuVZIqIiEi8cv1/29raGBoaoq2tjYULF2a2yXU569euh8KuwLPD5TGIbVAjd/8Zxft/Aswrsc0yYI+G5+6+Cnh5/aITkWbX29vLwMAAENSMbtmyhZGREebPn59wZCIiIpJFPT09TZkBLTT78NmsW78uqCHNWR8sj0PsgxqJiCShlUoyRUREROpl2UXLmHbDNHgUeB54FKbdMI1lF8UzYFVDpn0REUlCq5RkioiIiNTLgncFfYEXX7SY9d9az+zDZ7PsP5btWl5vypCKiIiIiIjILgvetSC2DGghNdkVERERERGRRChDKiIiIiIiIolQhlREREREREQSoQypiIiIiIiIJEIZUhEREREREUmEMqQiIiIiIiKSCGVIRUREREREJBHKkIqIiIiIiEgilCEVERERERGRRChDKiIiIiIiIolQhlREREREREQSoQypiIiIiIiIJEIZUhEREREREUmEMqQiIiIiIiKSCGVIRUREREREJBHKkIqIiIiIiEgilCEVERERERGRRChDKiIi0oTM7PVm9rCZrTGzTxR53czs4vD11WZ2XBJxiohIa9sr6QBERESkvsxsIvBV4AxgI3CnmV3n7g/krXYW0BM+TgT6w78iIpkzODjIihUrGBoaYtasWfT29tLT05N0WBKBakhFRESazwnAGnd/xN2fBa4Gzi5Y52zgmx64A+gwswMbHaiISK0GBwcZGBhgbGyM7u5uxsbGGBgYYHBwMOnQJAJlSEVERJrPLGBD3vON4bJK1xERSb0VK1bQ2dlJR0cHEyZMoKOjg87OTlasWJF0aBJB0zbZveuuu/5kZg8nHUeN/gz4Q9JB1Ej7kA7ah3TQPqTDS5IOoAGsyDKvYh3MbBGwKHy63czuqzG2VtUMv50k6LhVr5WOXRfwbJHlkz/0oQ8NV/herXTc6q2q9LVpM6TAw+4+J+kgamFmq7QPydM+pIP2IR2aZR+SjqEBNgIH5T3vBgpvyqKsg7tfDlwOzfH9J0XHrjo6btXTsauOjlv1qk1f1WRXRESk+dwJ9JjZoWY2GXgncF3BOtcB7wlH2z0JeMrdH290oCIi0tqauYZURESkJbn7DjP7O+BGYCJwhbvfb2bnhq9fBlwPvAFYA2wF3p9UvCIi0rqaOUN6edIB1IH2IR20D+mgfUgH7UNGuPv1BJnO/GWX5f3vwHkVvm1LHLuY6NhVR8etejp21dFxq15Vx86C9EhERERERESksdSHVERERERERBKRuQypmb3ezB42szVm9okirx9pZr80s+1mdmHe8oPM7BYze9DM7jezCxob+W4xVrUPea9PNLO7zexHjYl4T7Xsg5l1mNl3zeyh8Pt4deMi3y2OWvbho+F5dJ+ZfdvMpjQu8j3iHG8/FpjZ6vDxCzN7ZdRtG6XafcjY77rk9xC+noXfdblzKSu/63L7kJrfddIiHEczs4vD11eb2XFJxJk2tV4HWlnU9MjMXmVmz5vZWxsZX1pFOW5mdrqZ3RNe325rdIxpFeH3up+Z/dDMfhseO/WzB8zsCjP7vZWYAqyq9MHdM/MgGJhhLXAYMBn4LfCygnX2B14FLAMuzFt+IHBc+H8b8LvCbdO+D3mv/z1wFfCjrH0P4WtXAh8M/58MdGRpHwgmjn8UmBo+vwZ4X4q/iz8Hpof/nwX8Kuq2GdiHLP2ui+5D3utZ+F2X3IcM/a5LnUup+V0n/Yh4HN8A3EAwl+lJhedzKz7qcR1o1UfU9Chc7ycEfaPfmnTcST8innMdwAPA7PD5/knHnYZHxGP3T8C/hf/PAEaAyUnHnvQDeA1wHHBfidcrTh+yVkN6ArDG3R9x92eBq4Gz81dw99+7+53AcwXLH3f334T/jwEPEtyANFrV+wBgZt3AG4GvNSLYEqreBzNrJziRB8L1nnX30YZEvbuavgeCAcGmmtlewDSKzN3XIFH24xfuvjl8egfBXIORtm2QqvchY7/rUt9Dln7XRfchY7/rkt8D6fldJy3KteFs4JseuAPoMLMDGx1oytR6/rWyqOnR+cD/Ar9vZHApFuW4vQv4nruvh+DepsExplWUY+dAm5kZsC9BhnRHY8NMH3f/KcGxKKXi9CFrGdJZwIa85xup4ubTzA4BjgV+VZ+wKlLrPnwJ+Biws44xVaqWfTgMeBL4etg88Wtmtk+9A4yg6n1w9yHg88B64HGCuftuqnuE0VS6HwsJSq2q2TYutezDLhn7XRfuw5fI3u86fx+y+rvetQ8p+10nLcpxTMv1I03qci1rUeMeOzObBcwHLkNyopxzRwDTzexWM7vLzN7TsOjSLcqx+wrwUoLCyXuBC9w9yXQ6KypOH7KWIbUiyyoaJtjM9iUoXfuIu2+pS1SVqXofzOxNwO/d/a76hlSxWr6HvQiq+fvd/VjgaSCJvou1fA/TCUp/DgW6gH3M7K/rGFslIu+Hmc0luAH6eKXbxqyWfcgtz8zvunAfsvi7LvI9ZO53XeR7SNPvOmlRjmNarh9pUvO1rIVFOXZfAj7u7s/HH05mRDluewHHE7TCeR3wKTM7Iu7AMiDKsXsdcA9BmnAM8JWwRZCUV3H6kLUM6UbgoLzn3VTQpMrMJhHctC539+/VObaoatmHk4E3m9ljBE0LXmtm/13f8CKpZR82AhvdPVeL9V2CG9lGq2UfeoFH3f1Jd38O+B5Bv6AkRNoPMzuaoDno2e7+x0q2bYBa9iFTv+sS+5Cp33WZcykzv+sS+5Cm33XSohzHtFw/0qSma1mLi3Ls5gBXh9fKtwKXmtlbGhJdekX9rf7Y3Z929z8APwU0mFa0Y/d+gubO7u5rCMYZOLJB8WVZxelD1jKkdwI9ZnaomU0G3glcF2XDsP33APCgu38xxhjHU/U+uPsn3b3b3Q8Jt/uJuydRgl/LPjwBbDCzl4SL5hF0tm+0qveBoEnfSWY2LTyv5hH0XUzCuPthZrMJbq7f7e6/q2TbBql6H7L0uy61D1n6XZfZh8z8rsv8HtL0u05alGvDdcB7wtEUTyJo4vx4owNNmVrOv1Y37rFz90Pd/ZDwWvld4EPu/v2GR5ouUX6rPwBONbO9zGwacCKte23LF+XYrSdICzCzmcBLgEcaGmU2VZ4+eApGa6rkQTBy0+8IRsZaHC47Fzg3/P8Agpz5FmA0/L8dOIWgung1QfX7PcAbsrQPBe9xOgmNxlnrPhA0e1gVfhffJxxxMGP78FngIeA+4FvA3in+Lr4GbM4771eV2zZL+5Cx33XJ7yHvPdL+uy53LmXld11uH1Lzu076EeE4GvDV8PV7gTlJx5yGRz2uA636GO/YFaz7DTTKbuTjBvwjQSHhfQRdWxKPOw2PCL/XLuCm8Bp3H/DXScechgfwbYKxFp4juDdeWGv6YOGGIiIiIiIiIg2VtSa7IiIiIiIi0iSUIRUREREREZFEKEMqIiIiIiIiiVCGVERERERERBKhDKmIiIiIiIgkQhlSkSqZ2fNmdo+Z3Wdm/xPO7xXH58wxs4vD/083sz+v4j0+YmbvCf8/Moz7bjM7vMbYjjGzN+Q9f7OZfaLK95phZj+uJR4REUmvrKabSTOzi8zswvD/fzaz3jq855/Cv0p7JXHKkIpUb5u7H+PuLweeJZiDaVxmtlclH+Luq9z9w+HT04GKEtbw8z4AXBUuegvwA3c/1t3X5q1nZlbpNeEYgnm8crFe5+6fq/A9cts+CTxuZidXs72IiKReVtPNqlUa+3jc/dPuvqKO76e0VxKnDKlIfdwOvNjMOs3s+2a22szuMLOjYVfp5uVmdhPwTTM72MxWhuutNLPZ4XpvC0uOf2tmPw2XnW5mPzKzQwgS74+GJcynmtmjZjYpXK/dzB7LPc/zWuA37r4jrM38CPBBM7vFzA4xswfN7FLgN8BBZtZvZqvM7H4z+2zuTczsVWb2izC2X5vZfsA/A+8I43mHmb3PzL4Srl9qH79hZheH7/WImb01L9bvAwvq+L2IiEg6ZSLdDNe71cz+LUz7fmdmp4bLp5jZ183sXgtaHc0Nl78vrAH+IXBT+Pz7ZvbD8PP/zsz+PtzmDjPrDLf7GzO7M9yX/7UiNchhGvpWC2qB7wkf95qZh68fbmY/NrO7zOx2MzsyXH6omf0yfP+lBW/7fZT2SoKUIRWpkQWln2cB9wKfBe5296OBfwK+mbfq8cDZ7v4u4CvAN8P1lgMXh+t8Gnidu78SeHP+57j7Y8BlwH+EJcy3A7cCbwxXeSfwv+7+XEGIJwN3he9xfd57zA1ff0kYy7Huvg5Y7O5zgKOB08zsaDObDHwHuCCMrRd4Ooz3O2E83yn43FL7CHAgcArwJiC/RnUVcCoiItK0spRu5tnL3U8gKNT9TLjsvPBzXgGcA1xpZlPC114NvNfdXxs+fznwLuAEYBmw1d2PBX4J5JoGf8/dXxXuy4PAwj2P3q59WxXu0zHAj4HPhy9dDpzv7scDFwKXhsu/DPS7+6uAJwreTmmvJEoZUpHqTTWzewgu5OuBAYJM1rcA3P0nwIvCmkSA69x9W/j/q3mhKdC3wu0Afg58w8z+BpgYIYavAe8P/38/8PUi6xwIPFnmPda5+x15z99uZr8B7gaOAl5GkGl93N3vDPdtS67kuIxS+wjwfXff6e4PADPzlv8e6BrnfUVEJJuynG5+L/x7F3BI+H9+7A8B64AjwtdudveRvO1vcfexsInsU8APw+X35r3fy8NazXsJaiyPGm9nzOztwHHAJ8xsX4Lmyf8THuf/DPcFgkz2t8P/v1XwNkp7JVF1bdcu0mK2hSWTu5iZFVnPw79Pl3kvB3D3c83sRILS23vM7Jgy2+DuPw+b3Z4GTHT3+4rFCUwpsjxnV1xmdihBieqr3H2zmX0j3Nby9qNa+dtvz/s//5hNCeMVEZHmk+V0M5duPc8L98/FYs8pjD0/3duZ93xn3vt9A3iLu//WzN5H0P+1JDM7iqCG+TXu/rwF40CMFh7jPKXScaW9kijVkIrU108J+2GY2enAH9x9S5H1fkHQVIhw/Z+F2xzu7r9y908DfwAOKthuDGgrWPZNglLPYqW8EDT7eXHE+NsJEtGnzGwmQZMqgIeALjN7VRhnW9jkqlg8OUX3cRxHAMVuDkREpDllOd3Mj/0IYDbwcITtSmkjGGBoEuP06Qxrka8G3hPWuhIet0fN7G3hOmZmrww3+Tm7H798SnslUcqQitTXRcAcM1tN0DfyvSXW+zDw/nC9dwMXhMv/PRyc4D6ChO63Bdv9EJifG5whXLYcmM4LTXEK3QC8Jkrw7v5bgqa69wNXECRguPuzwDuAS8zst8DNBCWqtwAvC+N5R8R9LGcu8H9RYhURkaZwEdlNNy8FJoZNbL8DvM/dt4+zTTmfAn5FkMY+NM66bwEOBv4rN7hRuHwBsDBMq+8Hzg6XXwCcZ2Z3AvsVvJfSXkmUudfaCk9EkmTBKLVnu/u7y6xzLfAxdx9sXGSVs2CExLPdfXPSsYiISHNqpnSzHpT2StKUIRXJMDO7hKBZ7Rvc/Xdl1nsJMNPdf9qw4CpkZjOAk939+0nHIiIizamZ0s16UNoraaAMqYiIiIiIiCRCfUhFREREREQkEcqQioiIiIiISCKUIRUREREREZFEKEMqIiIiIiIiiVCGVERERERERBKhDKmIiIiIiIgk4v8HtmhwN6at+RoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Assign Initial Prototypes \n",
    "centroids = {}\n",
    "for i in range(K):\n",
    "    norm_por = np.random.random(); por = norm_por * (pormax-pormin) + pormin\n",
    "    norm_AI = np.random.random(); AI = norm_AI * (AImax-AImin) + AImin\n",
    "    centroids[i+1] = [por,AI,norm_por,norm_AI]\n",
    "    \n",
    "plt.subplot(121)                          # plot the training data and K prototypes\n",
    "plt.scatter(df_subset['Porosity'], df['AI'], c=\"black\", alpha = 0.4, linewidths=1.0, edgecolors=\"black\")\n",
    "for i in centroids.keys():\n",
    "    plt.scatter(centroids.get(i)[0], centroids.get(i)[1], color=colmap[i],linewidths=1.0, edgecolors=\"black\")\n",
    "plt.title('Acoustic Impedence vs. Porosity with Initial Prototypes'); plt.xlabel('Porosity (fraction)'); plt.ylabel('Acoustic impedance (kg/m^3 x m/s x 10^3)')\n",
    "plt.xlim(por_min, por_max)\n",
    "plt.ylim(AI_min, AI_max)\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=1.0, top=1.0, wspace=0.2, hspace=0.2)\n",
    "\n",
    "plt.subplot(122)                          # plot the training data and K prototypes\n",
    "plt.scatter(df_subset['Norm_Porosity'], df['Norm_AI'], c=\"black\", alpha = 0.4, linewidths=1.0, edgecolors=\"black\")\n",
    "for i in centroids.keys():\n",
    "    plt.scatter(centroids.get(i)[2], centroids.get(i)[3], color=colmap[i],linewidths=1.0, edgecolors=\"black\")\n",
    "plt.title('Normalized Acoustic Impedence vs. Porosity with Initial Prototypes'); plt.xlabel('Porosity (normalized)'); plt.ylabel('Acoustic impedance (normalized)')\n",
    "plt.xlim(0, 1)\n",
    "plt.ylim(0, 1)\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=2.0, top=1.0, wspace=0.2, hspace=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Assignment of Training Data\n",
    "\n",
    "All training data are assigned to the nearest prototype.  \n",
    "\n",
    "* recall we have a function to do this\n",
    "\n",
    "```python\n",
    "df = assignment(df, centroids) \n",
    "```\n",
    "\n",
    "* we work with the normalized features and visualize normalized and original features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = assignment(df, centroids)            # assign training data to the nearest prototype\n",
    "\n",
    "plt.subplot(121)                          # plot the assigned training data and K prototypes\n",
    "plt.scatter(df['Porosity'], df['AI'], color=df['color'], alpha=0.5, edgecolor='k')\n",
    "for i in centroids.keys():\n",
    "    plt.scatter(centroids.get(i)[0], centroids.get(i)[1], color=colmap[i],linewidths=1.0, edgecolors=\"black\")\n",
    "plt.title('Acoustic Impedence vs. Porosity with Initial Prototypes'); plt.xlabel('Porosity (fraction)'); plt.ylabel('Acoustic impedance (kg/m^3 x m/s x 10^3)')\n",
    "plt.xlim(por_min, por_max)\n",
    "plt.ylim(AI_min, AI_max)\n",
    "\n",
    "plt.subplot(122)                          # plot the training data and K prototypes\n",
    "plt.scatter(df_subset['Norm_Porosity'], df['Norm_AI'], c=df['color'], alpha = 0.4, linewidths=1.0, edgecolors=\"black\")\n",
    "for i in centroids.keys():\n",
    "    plt.scatter(centroids.get(i)[2], centroids.get(i)[3], color=colmap[i],linewidths=1.0, edgecolors=\"black\")\n",
    "plt.title('Normalized Acoustic Impedence vs. Porosity with Initial Prototypes'); plt.xlabel('Porosity (normalized)'); plt.ylabel('Acoustic impedance (normalized)')\n",
    "plt.xlim(0, 1)\n",
    "plt.ylim(0, 1)\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=2.0, top=1.0, wspace=0.2, hspace=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Update the Prototypes\n",
    "\n",
    "Now we reassign the prototypes to the centroids of the training data belonging to each."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "old_centroids = copy.deepcopy(centroids)\n",
    "centroids = update(centroids,pormin,pormax,AImin,AImax)\n",
    "    \n",
    "plt.subplot(121)                          # plot the assigned training data and K prototypes\n",
    "ax = plt.gca()\n",
    "plt.scatter(df['Porosity'], df['AI'], color=df['color'], alpha=0.5, edgecolor='k')\n",
    "for i in centroids.keys():\n",
    "    plt.scatter(centroids.get(i)[0], centroids.get(i)[1], color=colmap[i],linewidths=1.0, edgecolors=\"black\")\n",
    "plt.title('Acoustic Impedence vs. Porosity with Updated Prototypes and Vectors'); plt.xlabel('Porosity (fraction)'); plt.ylabel('Acoustic impedance (kg/m^3 x m/s x 10^3)')\n",
    "plt.xlim(por_min, por_max)\n",
    "plt.ylim(AI_min, AI_max)\n",
    "\n",
    "for i in old_centroids.keys():\n",
    "    old_x = old_centroids[i][0]\n",
    "    old_y = old_centroids[i][1]\n",
    "    dx = (centroids[i][0] - old_centroids[i][0]) \n",
    "    dy = (centroids[i][1] - old_centroids[i][1]) \n",
    "    ax.arrow(old_x, old_y, dx, dy,fc=colmap[i], ec=colmap[i])\n",
    "    \n",
    "plt.subplot(122)                          # plot the assigned training data and K prototypes\n",
    "ax = plt.gca()\n",
    "plt.scatter(df['Norm_Porosity'], df['Norm_AI'], color=df['color'], alpha=0.5, edgecolor='k')\n",
    "for i in centroids.keys():\n",
    "    plt.scatter(centroids.get(i)[2], centroids.get(i)[3], color=colmap[i],linewidths=1.0, edgecolors=\"black\")\n",
    "plt.title('Normalized Acoustic Impedence vs. Porosity with Updated Prototypes and Vectors'); plt.xlabel('Porosity (normalized)'); plt.ylabel('Acoustic impedance (normalized)')\n",
    "plt.xlim(0, 1)\n",
    "plt.ylim(0, 1)\n",
    "\n",
    "for i in old_centroids.keys():\n",
    "    old_x = old_centroids[i][2]\n",
    "    old_y = old_centroids[i][3]\n",
    "    dx = (centroids[i][2] - old_centroids[i][2]) \n",
    "    dy = (centroids[i][3] - old_centroids[i][3]) \n",
    "    ax.arrow(old_x, old_y, dx, dy,fc=colmap[i], ec=colmap[i])\n",
    "      \n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=2.0, top=1.0, wspace=0.2, hspace=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Repeat the Assignment of the Training Data\n",
    "\n",
    "Once again we assign the training data to the nearest prototype. \n",
    "\n",
    "* Note the prototypes were updated in the previous step so the assignments may change"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = assignment(df, centroids)\n",
    "\n",
    "plt.subplot(121)                          # plot the assigned training data and K prototypes\n",
    "plt.scatter(df['Porosity'], df['AI'], color=df['color'], alpha=0.5, edgecolor='k')\n",
    "for i in centroids.keys():\n",
    "    plt.scatter(centroids.get(i)[0], centroids.get(i)[1], color=colmap[i],linewidths=1.0, edgecolors=\"black\")\n",
    "plt.title('Acoustic Impedence vs. Porosity with Updated Training Data'); plt.xlabel('Porosity (fraction)'); plt.ylabel('Acoustic impedance (kg/m^3 x m/s x 10^3)')\n",
    "plt.xlim(por_min, por_max)\n",
    "plt.ylim(AI_min, AI_max)\n",
    "\n",
    "plt.subplot(122)                          # plot the training data and K prototypes\n",
    "plt.scatter(df_subset['Norm_Porosity'], df['Norm_AI'], c=df['color'], alpha = 0.4, linewidths=1.0, edgecolors=\"black\")\n",
    "for i in centroids.keys():\n",
    "    plt.scatter(centroids.get(i)[2], centroids.get(i)[3], color=colmap[i],linewidths=1.0, edgecolors=\"black\")\n",
    "plt.title('Normalized Acoustic Impedence vs. Porosity with Updated Training Data'); plt.xlabel('Porosity (normalized)'); plt.ylabel('Acoustic impedance (normalized)')\n",
    "plt.xlim(0, 1)\n",
    "plt.ylim(0, 1)\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=2.0, top=1.0, wspace=0.2, hspace=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Iterate Until Convergence \n",
    "\n",
    "Now we interate over the the previous set of steps:\n",
    "\n",
    "* assign the training data to the nearest prototype\n",
    "\n",
    "* update the prototypes \n",
    "\n",
    "We do this until there is no further chance in the category assigned to each of the training data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration:\n",
      "2 3 4 5 6 "
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Continue until all assigned categories don't change any more (code modified from Ben Keen, http://benalexkeen.com/k-means-clustering-in-python/)\n",
    "\n",
    "iteration = 2\n",
    "print('Iteration:')\n",
    "while True:\n",
    "    print(iteration, end =\" \")\n",
    "    closest_centroids = df['closest'].copy(deep=True)\n",
    "    centroids = update(centroids,pormin,pormax,AImin,AImax)\n",
    "    df = assignment(df, centroids)\n",
    "    if closest_centroids.equals(df['closest']):\n",
    "        break\n",
    "    iteration = iteration + 1\n",
    "        \n",
    "plt.subplot(121)                          # plot the assigned training data and K prototypes\n",
    "plt.scatter(df['Porosity'], df['AI'], color=df['color'], alpha=0.5, edgecolor='k')\n",
    "for i in centroids.keys():\n",
    "    plt.scatter(centroids.get(i)[0], centroids.get(i)[1], color=colmap[i],linewidths=1.0, edgecolors=\"black\")\n",
    "plt.title('Acoustic Impedence vs. Porosity with Final Prototypes'); plt.xlabel('Porosity (fraction)'); plt.ylabel('Acoustic impedance (kg/m^3 x m/s x 10^3)')\n",
    "plt.xlim(por_min, por_max)\n",
    "plt.ylim(AI_min, AI_max)\n",
    "\n",
    "plt.subplot(122)                          # plot the training data and K prototypes\n",
    "plt.scatter(df_subset['Norm_Porosity'], df['Norm_AI'], c=df['color'], alpha = 0.4, linewidths=1.0, edgecolors=\"black\")\n",
    "for i in centroids.keys():\n",
    "    plt.scatter(centroids.get(i)[2], centroids.get(i)[3], color=colmap[i],linewidths=1.0, edgecolors=\"black\")\n",
    "plt.title('Normalized Acoustic Impedence vs. Porosity with Final Prototypes'); plt.xlabel('Porosity (normalized)'); plt.ylabel('Acoustic impedance (normalized)')\n",
    "plt.xlim(0, 1)\n",
    "plt.ylim(0, 1)\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=2.0, top=1.0, wspace=0.2, hspace=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Clustering with scikit-learn function\n",
    "\n",
    "Let's repeat with the scikit-learn function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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xux1pFEVRFGXO8GZv8ETG90Rs5PaD4BVFURRlTvJmB7OJBuJP2CYvhPgOzoIDBAYGLp8/f74n4/Iqi8VCV38f/oHjixhZRswYtTpCgiccGqsoiqL4uKKiok4p5S2Tw7jDm8m6kfGVnK5XmLmFlPKnOAvmU1BQIAsLCz0fnZfYbDb+4ac/JThvAaGRkc5lVitVJ07xrV27SE5OvsMjKIqiKL5ICHGn8s6T8may/hj4gRDiTZwdzPpuqu06J+l0Ovbs2MFrH31EZ2gw6HRYO7vYvHSZStSKoihzlMeStRDiDZwztUQJ59zFf45zkgKklD/BWcZzO85qSibgG56KZaZJSUnhD7/1LWpqarBYLCQnJxMV5W6tfUVRFGW28ViydhVDv939Evg9T+1/pgsICCAvz535GxRFUZTZTtUGVxRFURQfp5K1oiiKovg4lawVRVEUxcepZK0oiqIoPk4la0VRFEXxcSpZK4qiKIqPU8laURRFUXycStaKoiiK4uNUslYURVEUH+fN2uAeMzAwwMWSEhrb2oiPimLZkiWEhoZ6OyxFURRFuSez7sy6u7ubn7z2Gkcb6ugMDuRUSzM/fu03tLe3ezs0RVEURbknsy5ZHz5xHOJjyVi0iKj4eNLyctGnpnDg6FFvh6YoiqIo92TWJevymlriUlPHLYtNSaGyvh7n3CGKoiiKMrPMumQdbDRiNpnGLRsZHibQYEAI4aWoFEVRFOXezbpkvXbZMhovXcJmtQJgt9loKLvE2mXLvByZoiiKotybWdcbfHl+Pn2DA5w8egyN0Yh9eJhVC3JZs2qVt0NTFEVRlHsy65K1RqNhy6bNrF25it7eXkJCQggKCvJ2WIqiKIpyz2Zdsr7OaDRiNBq9HYaiKIqi3LdZd81aURRFUWYblawVRVEUxcepZK0oiqIoPk4la0VRFEXxcSpZK4qiKIqPU8laURRFUXycStaKoiiK4uNUslYURVEUH6eStaIoiqL4OJWsFUVRFMXHqWStKIqiKD5OJWtFURRF8XEqWSuKoiiKj1PJWlEURVF8nErWiqIoiuLjVLJWFEVRFB+nkrWiKIqi+DiVrBVFURTFx+m8HcD9Gh4e5vDx4xSVl4OULFuwgM0bNmA0Gr0dmqIoiqJMiRl9Zu1wOPjtu+9S1NFG8vq1pDywnuKeLl579x3sdru3w1MURVGUKTGjk3VDQwONpiEyFy9G7++Pn15P+sKFtIyMUFdX5+3wFEVRFGVKzOhk3dvbizYk+JblupBgent7pz8gRVEURfGAGZ2sw8LCsPcN3LLc1jdAeHi4FyJSFEVRlKk3o5N1SkoKySHBVBeXMDI8zMjwMNUlJSQGBJCWlubt8BRFURRlSszo3uAajYYXnnyK46dOUnjmLBJYuSCXDWvXotHM6OMQRVEURRk1o5M1gMFgYMvmB9my+UFvh6JMoaGhIa5UVNA/MEByYiIZGRnqAExRlDlrxidrZfZpaWnh1++/hy00FD+jkSNXrpBZVMRzTz6Jn5+ft8NTFEWZdipZKz5FSsmH+z8nMHseUfHxzoXZ86g8e46S0lKW5+d7N0BFURQvUO2KXjQwMMCpM2f47MABysvLsdls3g7J6/r6+mgfHLyRqF2i01IpqajwUlSKoijepc6svaS5uZlfv/8+jshw/AODOHvqJIlFRbz07LP4+/t7Ozyv0Wq1SLsDKSVCiNHldrsdvV41gSuKMjepM2sv+fjAFwTlzCNj0SISM9LJXrWSFhwUXbjg7dC8Kjg4mMz4eJqqqkaX2W02OqqrWZ630IuRKYqieI9K1l4wMDBAa1/frU29KSmUXb3qpai8x2azMTAwMFrPfdejjxLU28/VEyepPn+BqqPHWD8vh5ycHC9HqiiK4h2qGdwLdDodQkocdjsarXZ0uc1qJXiONfWePXeOr86eYcQh8dcIHly1mpUFBXz35ZdpbGzEZDIRGxurKtIpijKnqWTtBQEBAeSmplFVUUla7gLAmajbq6p4aONmL0c3fYpLSvi08BzpKwowGI0MDw3x6bkzGPz9WbxoESkpKd4OUVEUxSeoZnAv2b51K3F2O5VHjlFTWET10WM8MH8B8+fP93Zo0+bImTMkLszD4Jp7PCAwkMSFCzly9oyXI1MURfEt6szaSwIDA/nG8y/Q2trK0NAQ0dHRhISEeDusadXd38+80NBxy4JCQqjq6/dSRIqiKL5JJWsvi4uL83YIXpOemEhXSwvRiYmjyzqaW0hPSrzNVoqiKHOPR5vBhRCPCiEqhBBVQog/neD+UCHEJ0KIYiHEJSHENzwZj+JbtmzYQG9lJU01tQz199NUU8NAVRUPrVvv7dAURVF8iseStRBCC/wrsA3IBZ4XQuTetNrvAeVSyiXAJuDvhBB6T8Wk+JbExES+++xzZGq0jFRcJVOj4zvP7iExUZ1ZK4qijOXJZvCVQJWUsgZACPEmsAsoH7OOBIKFs1RVENANqJqbc0hsbCy7d+zwdhiKoig+zZPN4InAtTG3G13LxvoXYAHQDJQCP5RSOm5+ICHEd4QQhUKIwo6ODk/FqyiKoig+yZPJWkywTN50+xHgIpAALAX+RQhxS5doKeVPpZQFUsqC6OjoqY5TURRFUXyaJ5N1I5A85nYSzjPosb4BvC+dqoBaYO4MNFYURVEUN3jymvU5YJ4QIh1oAp4DXrhpnQbgIeCYECIWyAFqPBiTco8sFgvFJSVcrq7GGGCgYPES0tLSvB2WoijKnOCxZC2ltAkhfgDsB7TAL6SUl4QQ33Pd/xPgL4FfCSFKcTab/4mUstNTMSn3xmq18pu336bRZiUqOYmukRFKP/2E7atWs2rFCm+HpyiKMut5tCiKlHIfsO+mZT8Z83cz8LAnY1Du3+XLl2m0jJC98kZijoiJ4YsTJ1myaBEGg8GL0SmKosx+qja4ckc1164RGj++0preYEAEGWlvb/dSVIqiKHOHStbKHYUEBmIeHBy3TEqJbdiM0TUJh6IoiuI5Klkrd7Rk0SIsra30d3cD4HA4aLhyhfSoaKKiorwcnaIoyuynJvJQ7igyMpKvPbaTDw98QZvdjsNqIycxkcd3bvN2aIqiKHOCStaKWzIyMvjRt79Dd3c3er1+zk3nqSiK4k0qWStu02g0qtlbURTFC9Q1a0VRFEXxcW6dWQshwnHW7x4G6iaabENRFEVRFM+YNFkLIUJxzjf9PKAHOgADECuEOA38m5Ty0LREqSiKoihz2O3OrN8FXgU2SCl7x94hhFgOvCSEyJBS/tyD8SmKoijKnDdpspZSbr3NfUVAkUci8rLh4WEGBwcJDQ1Fr9d7OxxFURRFuW0zeP7tNpRSnp/6cLzHZrPx5eHDnL1UhsZgQFgsbF29hpVqogpFURTFy27XDP53rv8NQAFQjHNmrMXAGWC9Z0ObXkePH+dMQz0ZD2xA5+eH2WRi77kzhIaEkJOT4+3wFEVRlDls0qFbUsrNUsrNQD2QL6UskFIuB5YBVdMV4HSw2+2cKi0hbfEidH5+ABiMRqKzczhRNCtb+2eFjo4Orl69SmenmlVVUZTZzZ2hW/OllKXXb0gpy4QQSz0X0vSzWq1YbHb0N031GBAUSH9t7bTF0dfXhxBCVQe7A6vVygeffkp5UyO64GBs/f0sTEnhicd2oNOpOj+Kosw+7vyyXRZC/Ax4DZDA14DLHo1qmhkMBuLDw+luayMiNnZ0eUdjI4tSUj2+/46ODj7a/zmNrokyUqOi2PXoNiIiIjy+75no+MmTXOnvJXvjAwghkFJSXlhEzOnTPLB+Vl2dURRFAdyrYPYN4BLwQ+BHQLlr2ayybfNmusrLabx6ld7OTmrLLqHr6GLdqlUe3a/FYuHV995lMDKCnAc3k715Ez0hwbz6zjvYbDaP7numOlNaQvKCBQghABBCkJS7gLMlJV6OTFEUxTPueGYtpTQLIX4C7JNSVkxDTF6RmprK9/Y8z7kLF+hobWd+fALLtz/m8Sbpqqoqhgz+zEtOBpyJJz4tjatt7dTW1jJv3jyP7n8mslito30LrtP5+WG2Wr0U0fTr7u7m5Nmz1DQ1EhkaxrqCAtLS0rwdlqIoHnLHZC2EeBz4W5xVzNJd16v/h5TycQ/HNu1iYmJ47JFHpnWfQ0NDaAMCblmuCTBgMpmmNZaZYuG8bK7W1JKSkz26rKWmhsVz5MCmu7ubn77xWzTx8UTmLqCrv59ffvIJzz70EHm5ud4OT1EUD3CnGfzPgZVAL4CU8iKQ5rGI5pj4+HisXT1IKUeXORwObN29xI65fq7c8NCGDeg7u6g+f56mmlqqis5j6Oll0xy5Xn3q3DlEXBzJ2fMwBgcTnZhIUv5S9h87isOhyvYrymzkTgczm5Sy7/r1QWVqJSYmsjAxgdLTp4lOzwAknTW1LEtPJy4uztvh+aTQ0FC+//LLXKmooL2ri5jUNBbMn4+/v7+3Q5sWtU1NRIxpVQAICg2l2WJhaGiI4OBgL0WmKIqnuJOsy4QQLwBaIcQ84A+Ak54Na+4QQrB7x07mlZVRfOUKQgg2rllLXl6ex/bZ29vL1aoqHA4H6WlpxMTEeGxf96KxsZFTRYV09vaRkZjIqoICwsLCxq1jMBhYumSJdwL0sujwMFr6+wgKvdGfwmI2o8P5uiiKMvuIsc2vE64ghBH4L8DDrkX7gb+SUpo9HNuECgoKZGFhoTd2PSuUXbrEewe/RBsViUarZaS9gy35y9mwbp23QwOgoqKCN77YT2hmBkGhoXS3tEB7B995/gXCw8O9HZ5PaGho4OcffkjCsiWEhIczMjxMXXExm7Lns+mBB7wdnqIokxBCFEkpC+5lW3fOrJcDfyal/C9jdpgPzKra4HOByWTi/S+/JHn1SgICAwGwZVs5ePwE2VlZ03aNvKS0lKPnztLd10d6UhIPrVtPQkICUko+O3KE+CWLCXGNMQ8KDaVBCE6ePTvtnf98VUpKCi888gifHz1K6/AwfgI2L13mMwdciqJMPXeS9X7gnBDiWSllm2vZz4DbTvSh+J6GhgY0YSGjiRqcQ570MTFU1dRMS7I+V1TEJ2dOkbhwIRkhIXS2tPLz997lO8/uISgoiD6zmdibisFExsdTc/mKx2ObSXJycsjOzsZkMuHv768qtynKLOdOb/AKnEO3Dgsh1rqWqd5mM5BGo0FO0FtYSgdajTsfhftjt9s5dOY0acuXExIRgVanIzY5iYDUFE6eO+dMOsJ5/XUs08AAkWGqCfxmQggCAwNVolaUOcCdX2gppfwUeBz4FyHED3CWHVVmmNTUVHRDJvp7ekaXmU0mbO2dzMvK8vj+TSYTw3bHuDN7gLCoKJra29HpdKxdvITai8VYRkYAGOzrp7PyKmvyVUOOoihzlzuH5AJASnlVCLEB+CXOaTKVGcbf35/nd+zkjU8+oT04EKHR4OjpZdemzURGRnp8/0ajEX+NwGwyYTAaR5f3dnaRERMNwAPr1yOl5OSJk9g1GgK1Op598CHS09M9Hp+iKIqvumNv8Ak3EiJFStnggXjuSPUGv39ms5m6ujocDgcpKSkEBQVN277PnjvHp4XnSFq0kMCQELpaWuitrOQ7z+wZN67cYrFgNpsJCgpCMw1N9IqiKJ7mkd7gQog/llL+byHE/51klT+4lx0q3mcwGJg/f75X9r2ioAA/Pz+OnjtHU18f6UmJPLn7qVsKwOj1evR6vVdiVBRF8TW3awa/Pg1m0XQEoswNQgiWLV3KsqVLvR2KoijKjDFpspZSfuL6/9fTF46iKIqiKDe7XTP4J9ym1/dsnHVLURRFUXzR7ZrB/8+0RaEo08BkMmGxWAgNDUVNTKMoykxyu2bwI9MZiKJ4itlsZt+XX1JaXYXQ6QjT+/P41q2kpaV5OzRFURS33HGctWumrf8fkAuMTukjpczwYFw+o7u7m76+PpKTk1WlqClWXV1NYUkxQ+YRcjMzWbZkiUemufxw316qR8xkbnwArU5HX2cXv/nkY77//AtERUVN+f4URVGmmjvZ55fAnwP/AGwGvsEcKDfa39/P//rHf+T0lXKEv4EgCd9//nkeucNkEiaTibJLl2jt7CQuKoqFeXkYxxQAUZxOnj7N/qJCwjMy0IeFcuBKOWUVFXx9z54pHbLV09NDRVMT8zZtHG36Do2KpC8+jotlpWzZtHnK9qUoiuIp7lSbCJBSHsRZQKVeSvkXwIOeDWtyAwMD1NbW4pigxvVU+uu//3uKhgbJe+F5Fj33LGGbH+B/v/4aJSUlk27T09PDj1/9NV9UXKFa2vmi4go/fvXX9Iwp76k4D2i+PHOajNWriE1OIjw6mqz8fJptFioqKqZ0X0NDQ2gDDLdcow4IDqanr29K96UoiuIp7iRrsxBCA1wVQvxACLEbiPFwXJMastv49f7P+eDTTz2WsFtbWzl3tZLsDevQ+vkBEB4XR0jeAt7fu3fS7Q4dP4YjNobMpUuIS0khc+kSHLExHDpx3CNxzlQdHR1ogoPQ39TkHRwbS821a1O6r6ioKOTQ8C2Tg/S1tpGRnDKl+1IURfEUd5L1jwAjzoply4GXgJc9GNNt6f0NzFu7hpKWZqqqqjyyj66uLnSBxtFEfV1gWBht3V2TbldWVUXcTZ2W4tLSKLt69a73/9Xhw3y0bx9lZWXYbLa72t7XGY1GbKbhW5aPDA0ROsWlTw0GA1tXr6bmzFnaGxvp6+qi+mIxUQ4HC/PypnRfiqIonnLHa9ZSynOuPwdxXq/2Oo1GQ2hSIuVXr5KdnT3lj5+eno7WNMxQby+BYWGjy7tq69gwf8Gk2wX4G7BaLOjGJHnLyAhGf8Ok29ysqqqK3+7biz4+Dv/AQC6eOU1ySTFfe/qZWVN+Mzo6mozoGGovlZO6YD4ajYa+zi6srW0sfmjrlO9v9apVREdFcba4mMGubjZnZJK/bJlHOrMpiqJ4gju9wQuA/wKkjl1fSunVmbfsNhv6gMA7r3gPjEYjX9/5OP/++T4iFy/GGBZKx9Uqgjq7eeo/7pp0u9VLlvDVpXKyCpaj0WhwOBxcu1TOQ0uWuLVfu93OhwcOELtsKSHhzvmb41JSuHqukLJLl8hftmxKnp8veHrnTj7Z/zmXDx1G6HSE+ul5aefjREREeGR/mZmZZGZmeuSxFUVRPM2d3uCvA/8JKAU826vLTZaREYYaGlm8e7fH9vH8nj0kJyby3mef0V1bx7b5C9jzo/9w26E+a1atoqevj/OHj6ALDsI2MEh+1jzWrFrl1j67u7sxIUl0JerrIpKTuFxdPauStdFoZM/uJxkcHMRisRAWFqZm11IURZmEO8m6Q0r5sccjcdOIaYj64yfYtmYtSUlJHt3X+vXrWb9+vdvr63Q6Ht++nQd6e+nt7SUsLIywMc3od+Ln54fDakVKOa73ssVsxmhwvyl9JpnO6TkVRVFmKneS9Z8LIX4GHARGri+UUr7vsahuIyIomD/61rcJDPRME/hUuNskPXa7zLh4rlVUkOKawnJkeJi+ugbyd+6c4igVRVGUmcKdZP0NYD7gx41mcAl4JVn7+/v7dKK+X7u3b+ftjz+i8shRNAZ/GDTx2Lp1pKamejs0RVEUxUvcSdZLpJSLPB6JAjibhX/nhRdpb29neHiY2NhYDLO0CVxRlJlDSkltbS3VVaVIKcnIXEhmZqaaFGeauJOsTwshcqWU5R6PRhkVE+O1ujOKoii3OHr0IJ0tx1m4IBQhoPT8RerrVvLQlu3eDm1OcCdZrwdeFkLU4rxmLQDp7aFbiqIoyvTo7Oykse4Ez+6eh06nBSAjPZZ3PzpHa2s+cXFxXo5w9rttshbO9o3vAvXTE46iKIria5qamkhP8RtN1ABarYaMVD+amppUsp4Gt03WUkophPgHKeXy6QpIURRF8S3+/v60m24tszFkchAdrioBTgd3qlCcFkKs8HgkinKfhoeHqa2tpbm5GSmlt8NRlFkjIyOD5nZ/rjXemBuhuaWbhiY/srKyvBjZ3OHONevNwPeEEHXAEOqateKDigqL+OKTg/jZ/bBJO5GJYTzzwtP3NN5dUZTx9Ho9j2x7iS+/eAuDvhIhwGQOYcsjX1OjVaaJuNMZiBBiwgG+UkqvXMcuKCiQhYWF3ti14qMaGxt59d9eJychF4Pe+cPR2HoNXazgm9/1iblnFGVWcDgctLe3A84RK6pE8N0RQhRJKQvuZds7vtKupBwG7HT9C3M3UQshHhVCVAghqoQQfzrJOpuEEBeFEJeEEEfuInZFAaC0uJRwfeRoogZIikumvaGDjo4OL0amKLOLRqMhLi6OuLg4lain2R1fbSHED3FO5hHj+veaEOL33dhOC/wrsA3IBZ4XQuTetE4Y8G/A41LKPOCZu30CijI8bEbvd+v0oVqhxWq1eiEiRVGUqeXOodE3gVVSyj+TUv4ZsBr4thvbrQSqpJQ1UkoL8CZw8/ySLwDvSykbAKSU7e6HrrjL4XBw7do1ampqMJvN3g5nys3PzaFzoH1cp7LBoQG0RqGKyyiKMiu408FMAPYxt+2uZXeSCFwbc7sRuHmuyGzATwhxGAgG/klK+aobj624qbOzk7d+8zaDnSbnmabWyvYnH2GJm3NszwTZ2dmkLiqlrKyE8IAIRqwWhujjya89gU7nzkdcURTFt7nzS/ZL4IwQ4gPX7SeAn7ux3UQJ/ebebDpgOfAQEACcEkKcllJWjnsgIb4DfAcgJSXFjV0r4Dyjfvv1dzAMB5GWOg+AYfMwe9/+jLi4OGJjY70c4XgOh4PGxkaGhoaIjY0lIiJi0nWtViuNjY1IKUlKSuKZ556murqa6qs1BAYZyVuYR2Rk5DRGryiK4jl3TNZSyr93dfxahzMBf0NKecGNx24EksfcTgKaJ1inU0o5BAwJIY4CS4BxyVpK+VPgp+DsDe7GvhWgpaWFgXYTqak3xkEGGAII0YZTXlY+rcm6r6+P84XnaaxvJCY+luUr8omKihq9v7+/nzd/8xY9zf3o8MMshyjYkM/Djz58y0QB9fX1vPv6e9iHBBoBGBw8+cJusrOzyc7OnrbnpCiKMl3cbSO8CLRcX18IkXL9OvNtnAPmCSHSgSbgOZzXqMf6CPgXIYQO0ONsJv8HN2NS7sBqtaIRt3ZL0Ol0jIyMTLCFZ3R3d/PLn/wav2EDESERVNXXcfFMMS9+63mSkpIA+PSjvdg6YGGqc/i+3W7n/OFiklOTyc290S/RbDbz9qvvkmhMITQqDICBoX7efvU9fv8//e6snj5VUZS5y53e4L8PtAEHgE+Bva7/b0tKaQN+AOwHLgNvSykvCSG+J4T4nmudy8DnQAlwFviZlLLsHp+LcpP4+Hgcehum4SHAmQA7u7po7LxGVvb0VR06fuQ4hpFAMpOzCA+NIC0xnRj/eL787CAAg4OD1F6pJznuxiUOrVZLfHgiF85dHPdYNTU1aMxaQoPDRpcFB4bgZ/Gnurp6Op6OoijKtHPnzPqHQI6UsuuOa95ESrkP2HfTsp/cdPtvgb+928dW7szf358dT23nozc+ZaTbSvWVGgbNA4TGB3Hgs4OEvxA+Ldd1q65Ukxo9/uAgOiKGi3WFWK1W7HY7AnHLuE2dVofVMr4FwGazIdByM4EGm8029cEriqL4AHeGbl0D+jwdiOIZeXl5PP313bQONJI6L5Wdj+3k6a3Poevz5+3X38HhuLU4/1QLDg1heNg0bpl5xIx/gD9arZbQ0FCiEyNp62wdt05zVxMLl40bmk9KSgrDDGG1WkaXWW1WzGKQ5ORkFBgZGaGmpoaGhgbsdvudN/BRUkp6e3sZHBz0diiK4nXunFnXAIeFEHtxzmcNODueeSwqZUq1tbaRm7aErOQbZ7cJMYmU1ZfQ3Nw8et3YU9Y8sIqPf7OPgAAjBr0Bq81KdfNV1j22avRseueTO3j9F2/QW9+LXqtn0DpAcm7CLUPMwsLC2Lz9AQ7tPUqoLhyAfmsP6x5ZS3R0tEefh68wm81UV1djMpmIj48nMTFxtBPepfJLfHbsM/QReuxWB/oRP57a9tSMm8KwubmZI4c/xGHtwO6A0PAsNj+4k5CQEG+Hpihe4U6ybnD907v+KTOMaWgYf92tb51Oo8VisUywxdRauHAhA7sGOHrgOMImsAs7Kx5czpq1a0bXiYuL4/s/+i5XrlxhcGCQhMQEMjIyJixpuHbdWtIz0qm4Uol0OMien01iYqLHn4cvaG1t5a1P30ITpcXP6Mdg+SDzorLYuW0nvb297D2+j9wteQQGOzvatTe1886+d/n+1783Y8acDw4OcuDzV9m8PpjkpBwcDgdll5r4bN+bPLvn27eMDlCUucCdoVv/fToCUTwnc14G548WkySTR3/ozBYzFs0I8fHx0xLDmrVrWF6wnL6+PoKCgggICLhlHaPRSH5+Pr29vXR0dNDW1jZpfPHx8dMWu6+QUvLJgU+IWRZHXLLzTNmx2EHxoYtcvnyZ3v5eQtPDRhM1QExiDC0VzTQ2NpKWlualyO9ORcUVstIcJCc5+1NoNBoWL0qmpv4qTU1NHm8JUhRfNGmyFkL8FPhnKWXpBPcFAnuAESnl6x6MT5kCGRkZZC/LpOxCCRHGKKw2C33WHh59auuESdNT9Hr9bZuqHQ4HX37xJeeOnccgArBKCwlZcTy95ymMRuO0xemrenp66LX2kZV8Yyy5RqMhPjuBS1cvER0Rg87v1q+0Vj+zaqSbTP2Eh/jdsjw0RMvQ0JAXIlIU77vdmfW/Af9NCLEIKAM6AAMwDwgBfoFzgo8ZS0qJxWLBz89vVs8go9Fo2P30E1QtraKivBJDgD8LF+/0uTPTS5cuUXSomEVpS9FqnT2+q2qusn/fF+x++olpi8Nms9HZ2Ym/vz/h4eHTtl+3TDSlrZRoNFoyUtMpOlyEPSd19PUbHhpmuH14Rl0miItL5krpMfLG9C202ew0tljJX+1bVfcUZbpMmqyllBeBZ4UQQUABEA8MA5ellBXTE57nVFZW8uW+r+jp6CEgyMC6B9eycuXKWXs9TKPR+HyFr8LT50mMTB5NNADpCRmUFRezfecI/v7+Ho+hvLycfR98jsMssTmspOWk8PiTjxMUFOTxfd9JREQEUcYommobSUx3NgXb7XaaK5p4bPljpKSkkBu3gIsHLhCRFonDZqO7poeH12ydUS0TGRkZlJakc/BwJbk50Vhtds4Xd5CasYGwsDBvh6coXuHONetB4LDnQ5k+9fX1vPOr90mLyCA1NRPT8BBfvX8EKSWrV6/2dnhzlsU8QohufFLUaDQgJTabzePJurW1lQ9f/4TM6GyCooOQUlJbVcOH737E11550aP7dtfOrTt565O36Grows/ox1DrIAuTF5KdnY0Qgm0PbyO3PperNVfR++lZ8PiCGTfzmFarZefjz1NSUsyZ4lJ0Wn8WLN5CTk6Ot0NTFK+ZGd1Dp9ipY6eJC0ogLMTZxGkMCGRe4nyOHzzJypUrZ3WTuC/LW5rLmc/OExJ0Y3hOW1cr8alx01JGtPhCCWF+EQQZnQcMQgjSEzMouXqBrq4un5gYJCoqiu++9F1qa2sZHh4mbkXcuGQshCAtLW3GdCabjJ+fH8uXF7B8eYG3Q1EUnzAnk3VHWydxQeN7lAYYArC0WxgZGZnWTlczSVtbG01NTeh0OhISEggPDx/XZH2/ClYUcOVSJZdqSwkxhDJsMeEwWnlx580l5T1jsH8Qg94wbpkQAj+Nn0/NA67T6Zg3b563w1AUZRq5nayFEIGu2bFmvOS0JFrLOjHG36hF3TfYR3B4MAaD4TZbzk1SSg7sP0Dh0Qv0tvRTW1uHmSGWrVrC40/vpGDF1Jz9GAwGXvnW16msrKTpWjOh4SHk5ua6db3YbDbT2tqKXq8nPj7+nvoeZM3PZP/Fr4iNulFAxGQ2gV7OmYIriqL4pjsmayHEWuBnQBCQIoRYAnxXSvm7ng7OU9ZuWMMvS38NrRAdHk3/0ADNvdd44qWds7aD2f2oqqris3cOYGm30VrfTlpiBvoAP5qvNPP5OwfQ++tZvHjxlOxLp9ORm5s7bqatO7l48SKff/gFOpsfNmkjPD6UZ154+rbzYU8kNzeXC5kXuFRdRnRoDCMjZrpHOnlsz6Po9aoekKIo3iPkRENBxq4gxBngaeBjKeUy17IyKeXCaYjvFgUFBbKwsPC+H6e9vZ3jR07QUHONyOgI1m1aS0ZGBg6Hg4sXL3L2xDmGTWbmL8xm3YZ1c7rM4X/9k/9K3clWzH0WAmUwdmknLCqEEeMQS1csQRMh+f4Pv+uV2FpaWvjFP/+anLhcAgzOyxdNbY2ISDvf+v437/rgy2KxUFZWRtWVaoxBRpYtXzqjhj0piuK7hBBFUsp7aop0qxlcSnntph+9mTs7gEtMTAxPPrP7luVfffkV5748T2psOlEB/lSdqefqlWq+/bvfnJPXspuammisbiEmPJZrvY0EBwYDgt6uboTWTqAxiOauO01t7jllJWWE+0WOJmqAxNgkSusv0tbWdtc1sfV6Pfn5+eTn5091qIqiKPfMrVm3XE3hUgihF0L8R5zzU886g4ODnD1WRG76IkKDwwgwBJCRlImtW1JaekshtzmhtbWVpJgU+iw9GAONDI+YEIDZasamsWC1WkjNTLnj43iKeXgEne7WaldaMbOqdimKotyOO8n6e8DvAYlAI7DUdXvW6e7uRo8/Ou34BodQYyhN15q9FJV3GY1GQkJDyJyfzkjAEHX9VVztuky7rZHk5BQ6Le1s2rLRa/HNm59F11AnYy/nDA0PgUESG6uqXSmKMju4UxSlE/CNihAeFhISgsVhxuFwjBtrPTg8SE5shhcj857MzEz04Roig+J4fEc6tY01lFWUESwMrHxkGes3rvPq9Ivz5s0jc2kqpReLiTBGYrFZGHD08cQLO1WnMEVRZg13eoP/GvihlLLXdTsc+Dsp5e94OLZpFxYWRm7+fCoKL5ORmIWfzo/WzhasAeYp6+080+j1el78xgt89O7H1DU2I4ywddeDPP7UTqKiorwdHlqtlqeffYrq5dVUVVYTYAwgb2GuGmqlKMqs4k5v8AvXe4Hfbtl0mare4JOxWq0cPXKUopPnsYxYSc9OY8ujD6kmVaC3txdA1WdWFEW5B57uDa4RQoRLKXtcO4twc7sZyc/Pj4e2PMTmBzdjt9vx87u189JcpZK0oiiKd7iTdP8OOCmEeNd1+xngrz0Xkm/QaDSqRriiKIriE9zpYPaqEKII2AwI4EkpZbnHI1MURVEUBXC/OfsK0HN9fSFEipTSe5UwFGUSdruzXo87E4w4HA7a29sRQhATE6NKzSqK4rPc6Q3++8CfA204K5cJQAJzs3u04pNMJhNffXmIksJSpJQsWDyfLY88NGmZ2MbGRt5/60NM3WZAEhITxFPPPak6EiqK4pPcuSj7QyBHSpknpVwspVwkpVSJWvEZDoeDN197i+qzdeTFL2FRwjJaLnXy2i9fn7CKmclk4s1fv024I4pFqUtYlLqUgOEQfvurN1XVM0VRfJJb5UaBPk8Hoij36tq1a3TUdZGVko1Oq0Or1ZKWkMZQm5nq6upb1q+urkYMaYkIjRxdFhMRg73fQW1t7XSGrijjSCnp6Oigo6ODOw2rVeYWd65Z1wCHhRB7gZHrC6WUf++xqBTlLvT396MX/rcs99cYRseGj2UymdBO8NHXoMNsNnsiRJ91c7U+xXtaW1s5eOAd/LQ9SAl2Inloy9Pq0owCuJesG1z/9K5/iuJTIiIiMMthpJTjOokNO0wTVllLTk7moDyM3WFHq3F2RLPZbZgZmhPTYTocDs4VneN08RlMw0OkxKWwae2mOfHcfZXZbGb/Z79h8zojyUnZANQ3dPD5vt/w/It/oErnKm4N3frv0xGIotyrhIQE0vNSuVxWTkpsKhohaGi7RmxGJOnp6ROuv2TtQopPlBIdFO1sehxqZ82WlURGRk6wh6nV1tZGR0cHRqOR1NRUt3quT6VjJ49TdK2QeZtyCAwOpLWhlTf2vsErT77iEyVk56KamhqS4kZITkoeXZaaEk1CdRU1NTXMnz/fi9EpvsCd3uDRwB8DeYDh+nIp5YMejEtR3CaE4Klnn+RM6hkunC3GYbeTv2Uxa9aunjQRbntsG9nzs7lUUo5Go2HLko0TJvap5HA42Lt/L5dbrhAUE8jIgIXA40b27NpDaGioR/d93cjICGcvnWXxtsXoDc5LB/Gp8YyYhim8UMijWx+dljiU8cxmM0GBtw4dDArUzLlLM8rE3GkGfx14C9iBc7rMl4EOTwalzB61tbWcOXGWnu5eMrPTWbl6pUfKlvr5+bF+/XrWr1/v1voajYZ58+Yxb968KY9lMiWlJVztq2L59oLR68R1l2v47ODnPPfknmmJYXBwEG2AdjRRXxcaFU77pfZpiWEqDA8PU19fj5SS1NRUjEajt0O6L/Hx8Rw5aGX5MgdarfOzYbc7qLtm5aHcBC9Hp/gCd5J1pJTy50KIH0opjwBHhBBHPB2Y4lnNzc3U1tSi99eTnZ3tkTO74uJi9r75GXHBiUQERFNxsoZLxZf5ne+9Mm1nkt5kNpux2WwEBQUBUFJZQnJuyrgOXSk5aRR9dA6TyTQtCSc4OBjHsAOzyYzBONpQRndbF6lRKR7f/1SorKzk5LF3SU5wAHDmJKxa+yQLFuR6JZ7u7m4sFgtRUVHodLf+pFosFiwWC4GBgZMW3omPjycypoC9+wtZuCAcKaG0vJuYhNXExMR4+ikoM4A7yfr6wNMWIcRjQDOQ5LmQFE+SUnJg/wEKj14gSBOCXdr5UnuIJ1/cRU5OzpTtx263c3DfV2TG5hBkdCaroMBgqq5VUXSuiAe3zN6rKCaTiS8OfUHFtUqEVhAVGMW2zY8iHRKNZvyPtRACKZxN5NNBr9ezdskaThw7SWZ+FoEhgbQ2tNBf3c/yp5ZPSwz3Y2hoiJPH3mbXtnjCwgIB6O838eG+90lKSiY4OHjaYunv7+fAF+8zYqrHYNAwMGRgzbpdZGc7O4hZLBaOHT1AQ/15dFqJzi+Stet3kJqaOuHjPbRlO1VV2VRWFQOCvKXbyMrKoqqqiuqqUkCSkbmIrKwsVW1vDnInWf+VECIU+CPgn4EQ4A89GpXiMdeuXaPwyAUWpiwZvZ47aBrko7c+4Ud/mj5lvU77+/uxDNkIigwatzw6PJraqjrYMiW78Unvf/o+Q+HDLN9VgFarpfVaK298+ibLc/K5UFFMeHTE6I9tc10TCWHxo2ff02HNqjUEGgM5e+EsNYPVpCemsW3Xo0RERExbDPeqtraWtGRGEzVASIiRjBRBTU0NS5YsmZY4pJTs//xtctJ6WZiXjRCC7u5B9h54k4iI3yUqKoqDX35KoL6cF55KR6/X0dzSzcGvfsP2nd+bsCOfRqMhOzt7NNkDHDq0n572kyxcEIYQgrILxdTXrWTL1sem5XkqvsOd3uCfuv7swzmZhzIJh8NBT08Pfn5+k5a59LbKK5UYRRBdXd1otVrCw8MIMgah6dTS1NQ0ZZ2sjEYjUiux2qz46W5MMzo4NEBMqud7XHtLa2srraY2lm+6MWVtXHIcvW09+On9iBexXDhwnuD4EEb6zdAteX7X89MaoxCCJYuXsGTx9CS2qeRwONBqbj2r1Gqnr3UCoL29HWlrYtHCG61RERFBLMzx58qVMhYvzqejrZSHn5k3etkjIT6CxQsGuXTpIhs33vlotaOjg+aGkzy7OxudznlgnZ4Ww7sfnaO1dTlxcXGeeXKKT5o0WQsh/hlnDfAJSSn/wCMRzVB1dXV8/O6nmHpNOJCkz09l5xM7pvWMyR01NTWcPn6GuOAEHFLiF6hlxeoCHFJOaXEMf39/VqzL5/yhUuYlZaP309M30EunuY3HVj88ZfvxNUNDQ/iHGG5Zbgw1Mjg0yDNPPENDQwOtba0ExwWTlZWFv/+tBV2UiaWmpvJhoY38pSMYjc7XbXjYQnW9nR1L06YtDmfv7VtHGgQF6eluG2BwcJDQYO0t36nwcCNNlZ1u7aO5uZn0FP1ooq6qbqWispbmpiYOHjzAM8/sueeWsN7eXpqbmzEYDKSkpEx4rV3xLbd7hwpd/68DcnH2CAfnfNZFngxqpunp6eHNX75NUnAamSnhOBwO6ipreffN93jlWy97O7xRra2t1JTXozfoiQyJwk+nZ2CwnyOHj5C9Ln3Ki2JsenATQiMoPF6E3eogJDKYp19+clYX34iOjma4Ywib1YbO78bXq7ellxW5zl7gaWlppKWleS9IL7HZbHR2dhIQEHDPHQxDQ0NZvOwx3v90H9kZfgghqaiykLt4O+Hh4VMc8eRiY2P5qhNMphsHDQBVNf2k52QRERFBd5/zQCIg4EZCbWjsIyauYKKHvIW/vz/tJuf50qkzV2huLGfZ4lCCA6Cl8xwff2Tjid0v3XWiPX78ELVVx0hJ1DE4ZOfEsWC2PfY1nxpjL6Wkt7cXrVbrs62U023Sd1lK+WsAIcQrwGYppdV1+yfAF9MS3QxRVlpGgCOI8BDnj4VGoyEjKZPi2gu0tbX5TLnA8rJyksNTiVseT3FJKQYZgAMHrcNNfHPL3X/p70Sn0/HQlod4YOMDjIyMYDQaZ31py5CQEArmF3Dh8AWS81Lw0+tpqm4kxBo8rcPEfE3ZpTIOnDgARrAO20iPSeOxrY/dUw/4ZcsKSE3NoLq6Cikl23Zm3lWicTgcXLp0iauV57DbraSmLWbp0uV3dZZqMBhYmv8oH3/2KUsXBhMQoKfiag9mezrz5s1Dp9OxcPEW9n3xGSvyowkKNFBV00FDczC717g3D1JGRgZnTvlz+UoTFRWXeW53AsPDI7S1h/DkrpUcPFzD1atXWbBggdtx19TU0HrtMHt2z8PPdTBZXdPGlwfeZc9z3/WJjmvNzc0cOfQBOLqw2SVBIRk8+NDjc2IEye248+ucAAQD3a7bQa5liktfbx8B+lt/dPTCj+HhYS9ENDGLxeKc5CI9h6T4FDp7OtBqtXQNpXj0qNrPzw8/P787rzhLbH5gM7HlsVwov8CgbYBlaUtZvnX5nHoNxmpqamLfqX0seDCPoJAgHA4HVSVX2XtgL8/seuaeHjMiIoKIiJX3tO2hrz7H1HeWVUtj0Gm1lFd8yScfV/LE7q/dVTW5ZcsKiIqK5crli1gsJpJTN7JgwYLRg94VK1YTGhrBxfIzmIcHSEhaxa7dK90+QNHr9Tyy7SXeeP3HYO3j0hU9VlsAC/JW4eenIz01iObm2rtK1lcri1mcFzaaqAEyM2IpKq6kq6vL62fXQ0NDHPj8VTavDyY5KRspJeWXm9i397fsee67s/5g/3bcSdZ/A1wQQhxy3d4I/IXHIppi0zFRQXpmOuWnKknkRvOuxWrBqrH41BjJeTnzuHCslERHEgGGAJLjUxgaHqLH0UlCgjr+mipCCPLy8sjLy/N2KD6h+FIxMfPjCApx9t/QaDRkLZ5H0SeF9PX1TesZU3d3Ny2N53juqezR4iMxMaHs3V9JTU3NXbd+JCcnk5ycPOn9N/fuvltxcXE898L32L/XzLycLIKDg0bPfvv6RzAG3t1r53DYRp/3WFqtwG6333OcU6Wi4goZqXaSk5ydUIUQ5OUmUV13lcbGRlJSZkYtAE9wpzf4L4UQnwGrXIv+VErZ6tmw7l9VVRVf7T9MW3Mb4ZFhbNz6AIsWLfLIvnJycijMKuJSVRmxYXFYbRbaB1vZvPMBn6qslJ6eTt6q+ZSeKSZUH4bNYcPEAE+88LiaKGCOstvt1NbW0tvbS0REBGlpaVN+cDtgGiAgOmDcMo1Gg1+Ac5az6UzW7e3tJCX43ZKwUpMCaGtr9MlLFXFxcQSG5FBT28niRc7CKi0tPVypcrDrybs7IExLX0j55UukpUaPvs+trb0MjwQTHR3tifDvisk0QGjwrS1QIcEaTCbTlO2ns7OT4otn6O1tJiwsgSVLV3m9VeFO3KkNLnCOis2QUv4PIUSKEGKllPKs58O7N3V1dbz1i3dJCU8jPzWVvsE+Pn5tL44XHB4Zh6nT6Xj+pecoKSnhSlkFwcZgHizYQEZGxpTv635oNBp27trB4qWLqKmqwRBgYP6C+T4xvlZKSXt7OxaLhdjYWHXwMAkpJZWVlZy/dAGLdYTstGzyl+bfU4/ywcFB3vzwTQb9hgiICMB01UTE2Qie3fUMAQEBd34AN2UmZ3Kq4TQxiTdamQb7B8HEtEycMlZgYCA9vbZblvf0WQiO9M1rokIItm1/lq8OfkzpO1fR+wnsMozNW16669K9OTk51NUu4cNPS8hI9WfQZKemXsODW7/mE03M8fHJlF04zMK8GzPo2Wx2GltsLF05NX1/Wlpa+OKzX5C/yJ9F80Joa7/M3o8v8PC2bxIfHz8l+/AEcacJzoUQPwYcwINSygVCiHDgCynliukI8GYFBQWysLDwtuv85hevYWsXxETc+HEYGOqn3d7C7//R7/lEJwrlht7eXt598z06rnWhEzocejuPPbmNhQsXejs0n3Pk+FHO1pwleWEKen89zdVNBA0F8uLTL971NfG9+/fSqGkme8mNZtrL5y6RE5jDQ5semrKYR0ZGeP291xkKNBGTEotp0ERHZTvbV29jYd70vsdSSt5+69/JTu1l8aIkhBDU1rVz/MwIzzz3A/r7++nq6iIkJISkpCSf+60YGBjAarUSHh5+z7FJKbl27RpNTQ0YDIFkZ2cTGBh4x22ampro7OwkNDSU1NRUjyR3h8PBxx+9gdGvmtz5UVitdi6WdhIRu8Gtsenu+OjD18jL6iIz40byr65p41JVJLue+NqU7GMyQogiKaV7wwFu4s4161VSynwhxAUAKWWPEMKnT3vaW9vJCB1fOjM4MISqhkpsNtuc7ejji6SUvPvme9jaYXHqMgBMw0N8/MZeov8g2mM96UdGRigpKaHqSjXBIUEsXb6UpCTfrqI7ODjI6bLTLNuRj5/e+RkOjw6n+GgxFRUVd3VwI6WkrKqMZbvGlxhNy82g9MvSu0rWIyMjaLXaSUcT+Pv78+JTL1JaVkpNQw2xAdE8sm2rV/pJCCF4bMcLHD60l4tvXkarFQQEJrLlkac59NU+BvsukRDnR0WZjdMiicd2POdTl7KmopyqEIKUlBS3r/9aLBb27X0H+0gVCXF+XKuxceZ0LDt2vjjldSQ0Gg07du6htLSEwtJStBo/cpc8fF/X/W/W3lbDYw9ljVuWlhrNV8evTtk+PMGt2uBCCC2uAimuKTOnr1TQPUhITqC7oYu46BtNGr39PUREhatE7WPa2trovNbNotSlo8uMAYGE6sIpLSklduvUJ+uRkRF+88vX6K0fIDoshsbaNkrOvM6O57axeLF7w2q8ob29HWO0cTRRXxeRGMG1lmt33RIhNBpubVmTbp+xtbW1ceDoARo7G9EKLYuzFrP5gc0TXsIYGRkhNiaWnOycaa3fPZGgoCB27NzD8PAwdrudoKAgzpw5iV6U88wT2aPP/2xhLSeOH2Trwzu9Gu9YDocDi8WCv7//tJ31FxWdIdRYw6ZHbpwAFV2o58Txgzzy6K4p359Op2PZsnyWLcuf8scGMBrD6O0dIjLyxuewr89EYOD0jdO/F+60Y/xf4AMgVgjx18Bx4H96NKr7tGHzetqGW2jrbMVqs9LZ00ltZw2bH9no7dCUm1gsFrTi1uEyej89w0Oemce3rKyM3oYBFqTnERUeTXJcCvNic9j/8QGsVuudH8BLAgMDnSVKbzLcP0Ro8N1dbxVCsDhrEXXlNeOW15bVsjj7zv06BgYG+O3Hv0Wkaln55GoWP7aUq8NVfPL5J+PWs9vtfPbFZ/z4jZ/w/qkP+PFvf8znBz73iZ7HAQEBo2eG1VcLyV+SMC4BLl2cTEP9RZ+IVUrJ2bOnePWXf8sbr/0Nb7z+L1RWVk7LvmuqzrN00fhWkMULE7nWUOwTr83dylu0nhNnmjCZRgBnYZvjpxvJXbjOy5Hdnju9wV8XQhQB19vFnpBSXvZsWPcnKSmJl777AkcOHqWi/hLR8dE899TTZGVl3XljZVrFxcXh0NsxmU0YDc7mRikl3UOdbJi/2iP7rK6oITJkfM9XY0AgsgO6urp8tuZybGwscYFxVBZXkrkwE61WS0dzO4MNgyzcc/fXfh9Y9wBvf/Q2F748jzHCyFCniVh9NGsfXnPHbUsvlRKQbCQhzfkjrvfXk7syj8JPz9Hd3T3aafH02dNc6atk+ePOSU3sNhulx0oIKwpj9UrPvL/3wuGwo9ONP3fRajVIaZ+g9WH6nTt3irZrX/DkjlSCgwNob+/jyyOvYzB80+PDmaR03HIW77wtfeK1uVtLlixjZGSYtz86jNFgx2TWkrtwK0uXeuZMfqq4W7LKCFxvCp+6bqIelJyczNdeedHbYSh3oNfr2b77UT55cy+hugj0fnq6hzpJXZjssWE0waFBdJobxy1zOBzYpA2D4da63r7kyR1P8vnBzyn6qBCNn4ZQfSh7tu25p5KMRqORr+/5Og0NDc6hW7kRJCcnu9W82t3XTVDE+OZsIQQBoQH09/ePJutzZefIfihntNiIVqcjK38ehccKfSpZp6Yvoaz8FGtXZ44uu1LRTHxCrtfrZtvtdspLj/LUzjSCgpyfz5iYUNasGKH44gmPJ+v0zHxKyo6xYd2Nk52yS00kJi3y+mtzL4QQrFq1jvz8lQwODhIUFDQjLo+6M3Trz3DWA38PEMAvhRDvSCn/ytPBKXPDokWLiI6OpqykjGGTmQ3zVzNv3ry7qiZ1N5bmL+XCyWIiTVEEGZ0VtWqaqsnMTb/roTDTzWg08uTOJzGZTFitVkJCQu7r2uX1WuV3KyEmgdqGOhLTbhQCslltDHeZxg3HMo+Y8TeMv4btH2DANOI7lf0ACgrW8MnH1ez7opKkeH86uqy0dASxc5f3J50ZGRlBoxkZTdTXRUYEMdDf7vH9FxSs5tNP6vh4XyWJ8To6Om10D0Sw8/GZPc+tn5/ftNaTv1/uHBY9DyyTUpoBhBB/A5wHVLJWpkxcXNy0NT/Hx8fzxIuP89mH+7F32bFJO1l56ezYteO+HldKSUVFBaUVpdjsdvKycsnLy/PIQYe3eyjnLsjlbMk5Ki9WkJiZxMjwCHUlteRn54/rQDYvdR6NNU2kZqeOLmusaSQndep6906FgIAAnnzqG9TU1NDZ2UZ8agQPbJnnE+P9DQYDQhNCV9fAuE5RjU3dREZ5voiLv78/u598ifr6ejo7O8lcEMrWjIwZeVY9k7kzzvoz4HkpZa/rdhjwmpTy/n7Z7pE746wVxR02m43u7m4MBsOUzOzzxcEvKG0pJWF+IhqtluarTST7J/Pkzt0+UXBiqg0ODnKm8AxXaq9g8DewPG85ixctHvdcu7q6+M37r+GfZCA0KpS+jl6sLRa+tvtrPlGMZ6YoL79EcdHbrF0ZTUR4ENcauzh3cYTtO7/tE5XHFPfczzhrd5L1h8AK4ADOa9ZbcfYIb4fpn9daJWvFF3V1dfHzD35O/vblaF1nHFJKzn9xnqc3POl2U7PJZOJs4VnKay6j9/MjPzefpUuWzuhkPzg4SElZCR3dHcRExrAob5HPzfM+E9TU1FBSfJyB/g6iY9JZXrBBJeoZxtNFUT5w/bvu8L3sSFFms9bWVgJjg0YTNTg7soQmhtDU0uRWsrZarbzxwRuMhFtIXpeM1WLjcOkR2jra2PbwNg9G71lBQUGsXb3W22HMeBkZGT5XwliZPu4M3fr1dASiKDNZQEAA1qFbx2iPDFoIinTvLLKiooJBwxCLC26Mcw7ZGELRp4Ws7ll9X51hrpeYrGuow+BvICc7Z87PDzydbDYbra2taLVaYmNjZ3RLieId7vQG3wH8JZDqWl8AUkp5x4t8QohHgX/COezrZ1LKv5lkvRXAaWCPlPJd98NXFN+QmpqK4Zg/DVUNJGc6hz+1N7VjbbMwb4t7nYBaO1oJjQ0bt0yr1RIYFUhnZ+c9J2uHw8He/XupaK8gLCUC64CFoxeOsmvzLp+cZWqmaG9vp6Ojg6CgIJKTkydNwLW1tRw9/C5hwSPY7A7MlnC2PrLHp6bPVXyfO83g/wg8CZTKuxgB7ypR+q84r3E3AueEEB9LKcsnWO9/AfvdfWxl7unt7eXcmXPUVzcQGRPJyjUrSExMvPOG92BoaIjT5047rxvr9OTnLiN/Wf5te3VrtVr2PL6HT774hKLLhSAgzC+U53bscbvndnhoOJXNt9YnNvcO31eJzpqaGio6K1n2yPLRhNKf0c+nh/by++k/UL1675LD4eDAF5/Q1X6B5EQ9VT02Tp2M5bEdL9zyPg0MDHD00G/ZviWaqCjn+U19Qwef73uNF772B+q1V9zmziflGlB2N4naZSVQJaWsARBCvAnsAspvWu/3cY7h9sosXorv6+np4Rc//hV6cwCRYVF0XO7h1xd+wzOvPDXlZ4YWi4Xfvv9b7DEOMjZlYh2xcLzkOO1d7Tz2yGO33TY8PJyv7/k6vb29OByOu54ZacH8BRy/cILGmkYS0hKw2+xUl1aREJx4X8PartZeJTozZtyZX0h4CCJE0NLSQnJy8j0/9lxUUlKM3XyBPU9mj76mxSUNHDm8jx0794xbt7Kygqw0RhM1QGpKNJFXrtLQ0KCuQStucydZ/zGwTwhxBBi5vlBK+fd32C4RZ6K/rhFYNXYFIUQisBt4EJWslUmcPnkaf7OR9CTnD1toUCiB/YF88ekBsn6UNaUTGlRUVDBsHGbRMtd14yAjizcupejTQtZ0r3FruNG9FlYxGo288PjzHDh6gHMXziCkhoWZeTy04/6mq9RqtNitt87h7LA5PFZ4ZioNDQ1x/NRxLlVfQqvVsmz+MlavXO21MdDVVedZmx837uBnYV4SF966gtlsHlcFb2RkGGPAra+xMUDDyMjILcvnCrPZTEdHBwEBAURFRVFRUcGVy2exjJhITF7AsmUrpnRO9dnAnWT918AgYADu5tsx0S/ozWfn/wj8iZTSfrsfXCHEd4DvAB4vraf4nprKOmIix08KHxYSTn1DLcPDw6PNzA6HA5vNdl8/4q0drYTGhY1bptFoMEYa6erq8vjY4OjoaF546oU7Tjt5N/Lm53FxfzGJ6YnoDf4AtF5rxWgP8Nk66NfZbDbe+OAN7DEOch9diN1up7ikmJa9LezZvefOD+ABdpsVrXb89WmNRqDROD+DYyUnp3H62EEWL3KMJveRESsNTTaWr/HMZRxfd/78OYov7Cc6QjAwaONak5XkRAdrVyZgDNBTWXWMDz+4xJNP/Q7+/v7eDtdnuPNLECGlvJeae43A2Pa1JKD5pnUKgDddiToK2C6EsEkpPxy7kpTyp8BPwTnO+h5iUWaw8MhQhpoGCQwIHF02YhlBq9eg1+ux2+2cOH6SM8fOYBm2EJsUy8OPbb2nA7uIsAgqmya4btw3fNvCKVJKenp6EEJMSQnDqfyRSkxM5IGFGzi67ygBsYHYR2zoTFqe3fGsz/dKrqqqYsjfxOKlN3rI561ZxPnPimhpaSE+Pv42W3tGWsYSyi4fYHP0jc9DVXUrwaGpt/RPSEpKIiQin08+O8+C7FBsNjtlVwaZn7eVkJAQzGYz1dXVmEwmEhISPNYPw1fU1dVx9fJent2VjtHoz9CQmX/6lzfJTJlPSnIU4LxkMHK0ivLySx6bJnMmcidZfymEeFhK+cVdPvY5YJ4QIh1oAp4DXhi7gpQy/frfQohfAZ/enKgVZfX61fz2p29jDAgkyBiE1WqhqrGCVQ+vQKfT8eUXX1L0VTFZidkYYgLo7Ong9Z+9yTd/8Mpd97hdMH8BJy6cpOFqPUmZydisNqpLqkgOSSY2duK5tVtbW/nkwCf0WvuQUhITGM3OrTvH1cj2ttUrV7MwdyFNTU3o9XqSk5NnROemjq4OAqPGD30TQmCMDKCnp8cryXrp0uV8+slVPvmsgpREA929VhpbA9i+49aijkIItj68g5qaXGprytFqdax9YBHJycm0tbXx+b5XSYobISRYcOKIlZDwpTz8yOM+fxB1r65cPs+yRaEYjc6D0a7uQRbnhWOzdjJsNhPguoSQmhxMdWM9oJL1de58W38P+GMhxAhgxc2hW1JKmxDiBzh7eWuBX0gpLwkhvue6/yf3F7oyV2RkZLDz+W0c3PcVlk4r6GDFluVs2LgBs9nMuRNFLEhZhJ/OOXNOVHg0JrOJonNFbHvs7oqJGI1GXnziBb48epCzF8+g02hZlLWITQ9tmnB9s9nMW5++RcyyOLKSnfWuG2saefPjt/juS9/xqYQYFBRETk6Ot8O4K5HhkZguX7hl+XCP2WvjxPV6PU/s/hq1tbW0t7cQlRDC2k3Zk87YptFoyMrKGjdFr5SSrw6+z8Y1BlJTnA2Q+Usd7Nt/kStXssnNzZ2W5zLdRkYGRxM1QKDRn74BB3HRzulTr+vpNRMYpMrRjuVOUZR7HjMipdwH7Ltp2YRJWkr5yr3uR5n9lixZwsKFCxkYGMBoNI5el+7p6UHj0I4m6uuCA0Nob+m8p31FRkayZ/ezWK1WNBrNbTthVVVVoYnSEpd849pvUkYSJde6qKurU3Oo36esrCyOnjtGdVkVqTlpOOx2qkuqiAuIIyEhwWtxaTQaMjMzyczMvPPKE+jp6QF7O6kpNw6eNBoNi/IiKassnrXJOil5AVerPycxwZmIIyODESKE86XtLMl3Huw0NXdzqcLK47sXezNUnzNpshZCzJdSXhFCTNgOIaU877mwZq7KykpOHjlFb3cfGdlprN2wlqioKG+HNStotdpbelqHhoYi/MA8MozB/0bv0e6+LhYuvb+zSHfmuB0yDaEPvLVDm5/RD5PJdF/7V5xnsS/sfp4jJ45w/sNCtBotS3KWsGHLhikdBXA7FouFCxcKqau5iBAaMuctZ+nS24+7d8+t8UsJYpY2gQMsXLiYjz4s5stDlWSkhdLfP4LZHo8+OJ/X36nBTyfR+kXx4NaXZ9T0ldPhdmfW/wFnD+y/m+A+iXO4lTLG+aLzfPbOAZLCU0g2ptFU3M6vLv2a3/ndb6gZhjzEz8+PjY9s4OAHR0iKSMFoMNLe1YY1wMzyFcs9vv/EhESOXT6OY0xvX7vdzlDrIPErp/966nRqaGjgZNFJOno6SYxJYG3BWo/0Lg8JCWHntp3skDumLUFf53A4+PSTNwkPqufBdbE4HJILJfvY39rA9seeuufHjYiIQOMXR3VNG5kZzr4QdruDkktd5CzcOlXh+xx/f3+e2P11Ll++TNW1agKMIezavZSoqCjMZjNWq5WgoKBpf59ngjvOuuVrfHXWLZvNxj/9738mJTAd45hey7VNtWStSuXR7Y94MbrZ7/Lly5w6dob+3n6y5meydr17Y6Lvl5SSD/d+SN1gHfHZiUiHg+aKJvJi8nh066Me37+3VFVV8f7h90lakkJYVCidrV20l7by4s4XvdLpy1NqamooPf8bHt9+o5XG4XDw7kdX2fjQd+/r4KSzs5PP9v6G6PBBQoI11DfaiI4r4MGHts3aDmZznadn3VLcMDg4iNVkxRgVOG55ZGgkjXWNXopqejU0NHDy2CnaW9pJSk1i7YY1d/1jZjabqaiooK+3j/iEeDIyMtxqblywYAELFiy419DvmRCCx7c9Tnl5OeXV5WiFlseWPzbjOnLdrUOnD5G5OouIGGeP9+RMIxqN4PjZ4zyz6xkvRzd12ttbSU4Yf5lDo9GQFO9HR0fHfSXrqKgonnvhB9TWOusFPJQXr+qFK5NSyXqKGI1GhA4sVgt6vxtf7v7BPuLS3JtzdmhoiPr6ejQaDampqTOqgk91dTVv/vwd4gITSAhOpfNKF78qe5WXv/eS22danZ2dvPbz17H3g7/WwCnrWeKzY3nuxT1eq1blDq1Wy6JFi1i0aJG3Q5kWVquVzr4usmKyxy2Pjo+mvPjmasIzW0hIGM119luWd/XYSMy493rt1/n5+ZGdnX3nFe/AZDIxPDxMaGioT41AUKaOeleniF6vZ/XGVZz8/BzzErMJMATQ1dtFl6WDx9fcefhQWVkZn76zFz9bAODAprfy5AtPzJhZkQ5+fojksDQiw5xnWomGJES74OihY+x54Vm3HmPfx59htISSmHqjMER5xSWKCotYs3aNW49RU1NDYUkhfYP9pCemsXL5ytsWM1Hunk6nI8gQyGDfAEGhNxJWX3cfEWGzq29GVlYWReeCuFLRRPa8eKSE0rJGhkZifKKaotVq5fChz2m6doFAo8A07M/yldtYuHBuHDjOJXe8MCKE2C2ECB1zO0wI8YRHo5qhNmzcwMbH11I3UMWF+nOYAwd54Vt77nhm2dfXx8dv7SUzcj7zUxcwPzWPtJBM3v/thzOiR7HNZqO9pX00UV8XFR5NQ821SbYaz2Qyca26kYSY8cNxEmOSKDlf6tZjFJcU8+7h97AlOohZEUuNvY5fv/NrBgYG3HsiiluEEKzNX0vF6QoG+wcB6Ovqpe58Hevy196yvsPhoL6+nvLycrq6uqY73Pui1+t5bOfXqayP5dU3K3n1zas0d6WxY+eLPnFd+eiRL9DJi7z4TCZP75rHrm1RlF54j/r6em+H5hVms5muri5stltr4c907pxZ/7mU8oPrN6SUvUKIPwc+9FhUM5RGo2HturWsXrP6rmpU19TUYHAYMRpulCoMCgxG16mnrq7O58dcarVagkODGDQNEmS8UW2qf7CfqFj3hq1pNBrQCBzSgVbcuEbtsNvxc6NZz2azcejMIXIfzCMw2NlvICQ8hEpHBecvnmfjho13+ay8p6GhgSNnjtDY1kRESARr89ewyMfOlPKX5iOl5MThE5htIwT7B/HY6u23jCvv7+/nrY/eZlA7iD5Iz9DJQZakLWHrg1t9Itm5IyIigl1PfA2z2YwQwmfqVZvNZhrqinjxmQx0Oud3JjTUyIpl4ZSVniE1NdXLEU4fm83GsaNfUldz1tXC4MeygodZsmT2VEBzJ1lP9I1Szee3odFo7uoaq91uR0w074kUt0wM4IuEEKzfvI797xxkXkI2xoBABob6aeyt59kn3RveYjAYyFk0j/qyOjKSnIUmHA4H1zqv8eiWO8861d/fj13vGE3U10UnxlB/aeacZTQ3N/PW/rdIyk9h5YZV9Hf38/m5/djtdpYuWert8EYJISjILyB/aT4WiwV/f/8Jh9vs+/IzdClali5YBjg/68WHLpJ0OYm8vLzpDvu+TFahzFvMZjMGf/DzG/9zHBIcwPBwr3eC8pKTJw9jN5/lxWcy8PPTMTAwzGcHPiIoKPSeC9f4GncObQuFEH8vhMgUQmQIIf4BKPJ0YHNJeno6wwxisVpGl5lHhrHohmfM0XH+8nwefvpBrplqKW4oot3WzK4Xd9zVNfdHtz9CQIKe0rqLVDRcoaThAgvXzmfJkiV33NZoNGIftmG7aSrIgd5+wkPGF1fo6+ujtLSU8vJyhoeH3Y5vqlRVVfHeJ+/x5gdvUVxcPK7J7lThKeIXJxCX7JyCMSwqjPlr53O08JhPHrhpNBoMBsOEiXpwcJD6jnpSctJGl2m1WpIWJFN8pXgao5ydQkJCsDmC6Ooaf5mntr6LuPj777Q2U1itVqqvnmHd6rTRA5fg4ABWFURRVnrKy9FNHXfOkH8f+G/AWzhL7nyBs164MkUiIyN5cOcmvvrkCAEEARKzGGLb048QHHz/PU6ngxCCghUF5C/PZ2RkZNIf8NsJCgrid77zCo2NjQwODhITE+N29TeDwcCSrCVcPltOTsF89P56ejt7abvcxtbtW0bXKzxfyFfnviIwIQiHXWI5/hm7t+wmIyPjrmK9V4ePHeFc7TkS5iei89PxVcUhrtRc4Zldz6DRaGjtbiNtYfq4bYJCgzHbhhkZGZlRIwQcDgdCK25p7tbqtIw4pv8gabbRaDSsXP0Y+796k/zFQYSFBlLX0E1VvZHdT93TUN4ZaWRkBJ3WjsEwvjUzNMSIaajHS1FNPXdqgw8BfzoNscxpq1evJisri5qaGoQQZGVlzchyexqN5r4Sikajuedetg9tegjtMS0X9l1EaiXBuiB2b3xitIZ0R0cHX50/xOJHl+If4Lzu2NfdxwcHPuQHr/yex69F9vb2cqb8DPk7lqNznQFEJ0Rz4cvz1NbWkpmZSVxELD0d3RiDbvRfGOwbwKAL8Jlrpe4KCQkhOjCK1oYW4lJudLJsrGxkfYazI5rD4cBqtc645+YrcnJyCA7+NmWl57hS201s3Hp2P5VPUFDQhOv39/czPDxMZGTkrBniFRgYiFYXTltbL7GxYaPLa2o7iEtY5r3AptjtaoP/o5TyR0KIT3CWFx1HSvm4RyObg6KiolQd8fug0+nYsnkLD6x7gJGREQIDA8ed1V2tvkpoSuhoogYIjQjFL9KPhoYGjw+Ta2trIzA2cDRRg7NFIjwpnIama2RmZrKmYA2v730drVZLdGIM/d39VBdWsbVgy4zpkDXW9ge388bHb9DV3I0h2EBfcy+JhgQWLVzE8ZPHOVt6Fqu0EhEUwdb1W0lLS/N2yDNOQkICCQm7brvO8PAwB7/8mJ6uCowBgsEhf1asfozc3JnVb2AiQghWrdnOgSOvs3zxEBHhgTQ09nCl2o9du1d7O7wpc7tDq9+4/v8/0xGIokwVvV4/YQc/KSVCM0HTvMZ5n6cFBARgNVlvWW4eNBMc5zwTSkhI4LlHn+Po6aMUnTlHeEg42woeZWHeQo/H5wmxsbF858XvUFlZSf9gP4mrE0lPT+fI8aNcbL3I/IdzCQgMoLO1k3cOvMNLO1/ySH3xue7LAx8RE1bLts1ZaDQaenuH2HfgXcLCwr06e9lUyczMxGj8NmWlhVTUdRAVvZonnlw+q2osTJqspZTXO5EtlVL+09j7hBA/BI54MjBFmWpZGVmc/PQklhwLen9nMh/sH8TSMTItBS6SkpIItBlpqKwneV4KQgg6WzsxNw8zf+P80fWSk5N5MflFj8czXYxGI0uXLh29bbFYKLxcyOJti9EbnK0cUXFRmHKHOHvhLI9vU412U6mvr4/e7gq2PzhvtHUmLCyQpQsDKb90flYka4D4+Hji43d6OwyPceeixcvAP9207JUJlimKT4uNjWVt7lpO7D9JUGIw2B2Ymk3s3LRzWoblaDQann38WT754hOKKgoRWkEggTy77dlJrzHORkNDQ2j8NaOJ+rrQiFC6rs2soikzgdlsJtCoveUySkhIADWNfV6KSrlbt7tm/TzwApAuhPh4zF0hgPpGKT5NSklpWSlnS87SP9hPRlIG61etZ92adeTMy6G+vh6tVkvmxsxp7XEfFhbGS8++RG9vLzabjcjISLd6zUspKS0t5Xz5eUYsI+Sk5bCyYCVGo/GO2/qa4OBgNBaBadA0riNdZ0snqTHeL+E520RGRtI/4Edfn4nQ0Buvd1VNNwlJK70Y2fTp6+ujpaUFf39/UlJSpmAu8ul3uzPrk0ALEMX4Oa0HgBJPBqUo9+vUmVOcvHqKjPxMkoNTaK5v4TcfvMY3nnnFJzryhYWF3dX6Xx35iovNxaQtTiPSX8/lqitcfe8qL+952acnOZmITqdj48qNHDx6kNSlaQSFBtHa0MpAdT8rnlnh7fBmHZ1Ox4rVj7H3i3dZtiiY4GAD1bU9tHXHsGajb1XG84RTp45RefkgKYl+DJnsHD8awrbHvub134C7dbtr1vVAvRBiCzAspXQIIbKB+YB7xZqnQVdXF4e+PETlpSqMQQGs2rCSVatWzcies8rUsFgsnCw+ycJHF4/2/E7LSaPKYuV88QUe3LjZyxHenf7+fooqisjfuRyta7jN/OULKDtRyuXLl90qGuNr8pfmE2QM4nTxGdoG2khPSOOJp3bd9UGM4p68vIWEhYVTfuk85mv9JCStZPUDi3yuKttUq6ur41rNlzz35Dz0eud3p6a2jQNfvMNzz3/vrmtBeJM716yPAhuEEOHAQaAQ2AN4vQfMwMAAv/p/rxJoDWFh/FLMFjNHPjxBf+8Aj2x72NvhKV7S19eH1qgbN0QLICI2guYrzV6K6t51dXUREGUcTdTXhcWH09zezBJmXrIGyM7OnpLpIRX3JCYmkpiYeOcVZ5GrlSUszgsdTdQAGemxFBVX0tHRMaPmD3fn9FNIKU3Ak8A/Syl3Az4xs0TxxWK0Jj3J8c5rEIEBgeSmLaTo5AUGBwe9HZ7iJcHBwViHLFgt44dJ9Xb0EhPu3tziviQoKAhz3/Atw8sGewYJCwnzSky9vb1cvHiRkpIS9V1TfJbdbh2d5GQsnU5gt986T7kvcytZCyHW4DyT3uta5hOlb1qb2wgNDB23TKvV4ocfvb293glK8TqDwUDBggIunSzDNGhCSklrQws9V7vJn4Gz8ERHR5MSlsKVwsvYrDaklLTUt2BuNLEwd/rHXxeeL+Snb/+Uk02nOF5/nB+//mMuX7k87XEoyp2kpedRXtEzrq5+e3sfgybjjDqrBveS7o+A/w/4QEp5SQiRARzyaFRuikuIpam0hJjI2NFldrsdK1Z17WuO27RhE4azBs4ePItpZJikmESef+z5Gdep5Lpd23dx8MhBLnx8HodwkBARz/M7n5/22vFdXV18VXSIxY/cKNk62D/Ip1/uJTUldUb2Tvc1vb29XLlSgcViIT09jaSkpBl1bdWXZGdnU1uzhA8/LSEzzcCQyU5VHWx66MUZ1yNcTEflpqlUUFAgCwsLAec165/+888ItIaQEJOI2WKmprmKZZsWq2vWCuAc8uRwOGbcF3MyVqsVm802af11h8Nx2ykr79fZc2cp6rrA/Pz545aXnShly/yHWLBgwZTvcy6pqKjgvQ8/xz8wCq3Wj6H+dpYuymT7tkdVwr5HUkoaGhpobKzDYAgkOzvHaxMkCSGKpJT3NMvKHc+shRCHmLg2+IP3ssOpFBwczMvfeYnDBw9TdukixqAANj6xjlWrVnk7NMVHCCFmTaIG8PPzw8/P75blUkrOFZ3j1MVTmG1mQgwhPLjmQXJycqY8hslyhkom98disfDhJ1+QOi+foCBnmUy7PYuLpafIXVBHenr6HR5BmYgQgtTU1Bkz3fBk3GkG/49j/jYATwG2SdaddlFRUTy952lvh6EoXnW28CzHK4+Ts3kBgcGB9HT08OHxj9jj/+yUTo6RmZHJ4YtHMOeYMRidw34G+wYwt5tJeeRGQZO+vj46OjoICgpStb7d1NzcDBrDaKIGZx+c0PB4Kq9WqWQ9x7kzRWbRTYtOCCFUXXBF8REOh4PTxadHEzVAeHQ4KUtSOHX+1JQm68jISB4qeJCD+w8SlBiEwwHDLSYe37QTo9GIw+Hg4OGDnK+6gDEqEHPfMAlB8Ty548kZNRe3N2i1WqR03LLc4XCg093amqLMLe40g0eMuakBlgPqUFlRPGRwcJCiC0VUN1YTEhhCweKC2yZci8WC2WYeTdTXhUaGUlNaM+XxLV+2nKyMLOrq6tBoNKQ/mD5a27ysrIyS1lKW77hRwKXywhW+PPwlO7f59iQLFouFwcFBAgMDvTK/dmJiIkZ/qKosp7+/j6EhE+ER4VhM3eQ+vm7a41F8izvN4EU4r1kLnM3ftcA3PRmUosxVQ0NDvPrOq2gTtMQujWNowMTbX73DIwUPs2TxxMVP/P39CTGE0NvZS1hU2OjyjuYOkmI8UwQjNDR0wsppFy5fIHVR2rgCLpmLsij6qIiHRx6eNAk6HA6Ghobw9/ef9vKpUkrOnDnLkWNncKAFaWPNqmU8sGG9xysh9vf3Mzg4SEREBAaDgWVLcvnbf/oZAcHJ+AcYqaouJDcrRo1uUdxqBlcXShRlmlwovoCI05K9zNnbOjQyjNDIUA4e/Iq83Dx0ulu/skIINq3exCcnPiV5aQqhEaF0tHTQcamdR3c9Mq3xj1gshOnHN9lqtFqEhkmLUJRfLuerU18xLM0IGyxfsJwH1j0wbR0DS0vL+PJIEdkLVuJvCMBiGeHkuQsY/P1ZvdoznVUtFgv7PvuCS5er0fkZcNjNbFy/krNFpTy95xtYrXbsdjthYVuoq77MxeIS1ngoFmVmcKcZ3A/4PvCAa9Fh4P9JKa2TbqQoyj2pb2kgOnt8lbXA4EAwOMffTjZOfMH8BRj8DZwqOkV1cRXJsUk8uuthYmNjJ1zfUxZkzKfkaim5K/NGl7XUtxAXHjfhGOyGhgY+PbmX7HU5hEaEYjGPUHymBE7A5gfuvoa7lJK2tjasViuxsbFunaWfOlNEcvoC/A3Oa+p6vT/pmQs5cbpoNFkPDQ3R29tLaGjolExn+uXBQ1TWdpK79AE0Gg0jI2Y+3X+UkeFBsnLHN3lHxyZQVVWnkvUc504z+I8BP+DfXLdfci37lqeCUpS5KiwolI6+LiJjI0eX2W02rCbrHTtopaene73HcEF+AVc/uErJsWLC4sIY6h1ipHmE53c+N+H6Zy+eJWFRIqERzkqEeoM/C1YtoOizItatXjeabM1mM1VVVQyZhkiIT5iwUEh3dzfvvPchXb1mNBodQo6wc/sWcnNvP/a7r3+AyPjxCTjAGIjJZMJms3Ho0BHOFpWi0xuxWYZZvmwBW7c8dM9n/haLheKSK8xbuG60md3f30BSag6HvvwIu90+7rGHh4dJiAqc7OGUOcKdZL1CSjn24tRXQohiTwWkKHNZ/uJ8frP3NcIiQwmNDMNmtVFRVMHC9DwCA33/BzsgIICXnnmJiooKmtuaCYsJI3dj7qRnoz39vcTkjD/71xv8kTpngtbr9bS3t/PGx2+gidKhD9Jz/MoJ0sPT2LV912hSczgcvPXOh6CPIm+Jczzt0NAAH35ygJiY6NEWiY6ODq5cqcDusJOVmUlSUhKZ6Sm0tjaRlHLjQKe9rZnkxHgKi85zrria+YvXo9P5YbfbOF92npDgs6xdu+aeXiOLxYJDcst4+ZCQMCLCQ6itLicjKw+NRoNpaJDu9np2bn3invalzB7uJGu7ECJTSlkN4Co3OrMqoCsz1vUKe3Ol4EZ8fDy7Nz7B/uNfcFVeRVocLM5czIMbvV6DyG1+fn4sXLiQhQvvXLc8NT6FhqZrhITfGFvc39NPgDCMJvhPDnxKzNJY4lLiAZCLJMWHL3Dp0iUWL14MQEtLC70DFnIX3yh8ERgYjDEklvLyyzzwwAaKzl/gsy+OEhQaBwiOnyphzcpFPLBhLb989S3qaiyERUTT39vNQG8jL7/4NO+8/wmpGXmjQ6e0Wh3pGbmcPnvhnpN1YGAgEeHBdHe1ExF5oz51e2sju3c+yuDQMJcuHEHn549G2Ni5bSPJycn3tC9l9nAnWf8n4JAQogZnj/BU4BsejUqZ8wYHBzl07BDlNeUIIViUtYiN6zfOidrT8+bNIzMzk4GBAfz9/Wf1nMMrl6/k8ruXqaaK6MQYBvoGaCptZOfaHWg0Gnp7e+ke6SYjJXN0GyEEiTlJlF29kawtFgta7a0/Z35+/piGzQwODvL5gSNk564evTZts2Vw6uwpchfk8O3feZHCwvM0XGskIzGalbtfIDo6GpNpGINh/OUHf0MAJtPwPT9nIQTbH32I3771EQP9vRgDQ+jpasOgM7N+/eMEBQXR39/P8PAwERERE1asU+Yed3qDHxRCzANycCbrK1LKEY9HpsxZNpuNNz54ExkP+U8UIKWk9lI1b3/0Nl/f83WPD6fxBRqNhtDQ0DuvOMOFhYXx8tMvc7boLPXnGggPCeP5rc+RkuKshiaEQDokUspxrSsOh0SrvfE5iI+Px24Zwmy+kVyllPR2t5C1aSvXrl1DbwgbTdQAOp0fgSEx1NTWkZ6WSnNLG00t7XR29xIYZGT9unBy5mXQ0tRAcuqNg4XW5mtkz7u/vgGpqal8+3de4OLFErp7eslblcOSxYtGD0ZDQkIICQm5w6P4vitXrnD02Gk6OrtJSIhl88Z1U1qkZy5xpzf47wGvSylLXLfDhRDflFL+2x02VZR7UlNTg8lgYvGYccXZy+Zz8csLNDQ0qC/7LBMWFsbDD0088U5oaCixwTE01jSSnOlsCrbb7TRdaWTb0kdH1zMYDDz68Eb27j9KSEQifn5+9HQ1k50ZR0ZGBtXV1Tgct169s9utDJtMvPrbd4mIyWJxQS6WETOnCi9hMg2zaeN6fvnqm1RVDhMaFkl/XzeOkW42P77nvp93VFQUW7bMnMsbd+vSpXLe/+RLktPyWJC0mO6uDl578yO+/sLu0YMxxX3uNIN/W0r5r9dvSCl7hBDf5kbvcEWZUr29vRjCb+35bAg30NfX54WIFG/auXUnb370FqWNxeiMfgy1DZGXmMv8+eNn/lq2bClxcbGUlZVjHhnhwbWbmTdvHhqNhtTUVDRymN6eLsLCnT3tTUODWExdDA+HYQiKJTbOWUDG3xBATu5SLhQf54EN6/jut75OcXEJza3t5KSmsGTJjllx1utph46cIC1zMSGh4QBEx8ThcNg5cuwUL72okvXdcidZa4QQQrp6+gghtMD0lhhS5pTIyEhM1UO3LDd1mIhYGDHBFspsFhERwXde+jZ1dXWYTCbiVsQRExMz4brx8fHEx8ffslyv1/P8s0/w5tsf0tbsj0SDwzrIEzsfpqS0nODg8HHra7U6dH4GBgYGiI+PZ/16Ve7zbthsNrp7+knOGv+6hkdEUVtR7aWoZjZ3kvV+4G0hxE9wlh39HvC5R6NS5rS0tDTCz4Zz+dwl0nLTkRJqyqqJN8aTlJTk7fAUF4fDgc1mm5byoDqdjqysrPt6jKSkJH74+9/l2rVrOBwOkpKSMBgMdHd3c+ZiLVHRN4aQWS0W7LZhVebTxW6309raihCCuLi4O/Yb0el0hIeF0N/XM3pmDdDT3Ul83MQHWsrtuZOs/wT4Ds4qZgL4Avh3TwalzG1arZbndu/hxOkTlB0sQwgNS7IXs2bVmmkdwlVRUcGJohN09HQQHx3PAysfUNfLcdXSPneGMyVnMFlMRIVE8eCaB8nMzLzzxl7m5+dHRkbGuGVLly7hXFEJ9bVXiYtPYnjYxLX6Ch5Yu1zNFAbU19fz3gd7sdids4IFGjQ8+/SuO059unnjWt7/5CCpGQsJCQ2nu6uD9uarvPT8E9MT+Cwjro9jdXsDIdYDz0spf88zId1eQUGBLCws9MaulTmk/HI5n57aS8aKTMKiwuhq7aK+qJY9W/fM+Ens79fJ0yc5VXOanFXzCQwOpLu9i+rT1bzw6PMztuWjt7eX4ydOUVFZQ1BQIKtX5rN48SKPHBxaLBZMJhNBQUET1nr3JUNDQ/zzv/2c+NRFhIU5L0F1drTR11HFD373W3dsVbl8+TJHjp2mvaOLpMQ4Nm9c5/Uqe94khCiSUhbcy7ZufVKEEEuB54E9OGfdev9edqYoM8XRs8fIWjWP8GhnE15MYgwOh4PjZ4/P6WRts9k4XXKaBVtzCQh0nnVGxEQyvGiYM+fPzNhkHRYWxo7HtrHjMc/tw+FwcOLkSU6cLMKBFj+tZNPGtawoWO65nd6nqqoqtPrQ0UQNEBUdS2f7Nerq6sjOzr7t9gsWLGDBgtuXe1XcM2myFkJkA8/hTNJdwFs4z8Tvvrq+oswgdrud7oFucqLH9zaOiIngUmGpl6LyDWazGZuwjybq60Ijw2iuaPRSVDPD2bPnOHqqjHkLVuPvb2DYNMTnX54iKNDoswlteNiMVnfrtKZanR6LxeKFiOau2/USuAI8BOyUUq6XUv4zqsyoMgdotVoigsPp7ewdt7yno4eYiLndOcZoNGLQGBjoHRi3vKO5g0QPzZ09G0gpOXG6iIysRfj7OyvSBRgDSUqdz4lT57wc3eRSUpIxDXaOm97UarViHuomMVG939Ppdsn6KaAVZ6nRfxdCPISzg5mizHobVmzg6pkKejp6kFLS0dJBw/k61q9Y7+3QvEqj0bBp5UaunCino7kds8lMw9V6eq50sWq5msJxMna7naGhYYyB4yc0CQwKpqe330tR3VlCQgLLl8yjvOQUTdfquNZQQ0XZaR5Yl094ePidH0CZMpM2g0spPwA+EEIEAk8AfwjECiF+DHwgpfxiekJUFPd1dXUxPDxMVFTUfdXUzsvNQyM0HC86zpWey8RHxfP0g0+r3uDA4kWLMQYYOXXhFC19zaTGp7LtiUeJjo6+88ZzlE6nIzEhho72VqJjbvSi7mhrITPdtwuEPPrIw2TPy+LylUq0Wi25W3fO6X4b3nJXvcGFEBHAM8AeKaVX6uSp3uDKREwmEx9/9jHXeq+hD/LH0mNh04qNrFi+wtuhKQoADQ0N/OaNDwiNTCE0LIKerg7MAy184+Xn1IHOHHE/vcHveuiWt6lkrUzk3Y/fpcvQzbwl2QghMJvMlB4q4ZlNT8/poSKKb2ltbeXsuSLa2p1DmVauWE5kZKS3w1KmiceHbimKLxscHKSmpYblu1aMjos1GA0k5CVysfyiStaKz4iLi+PxnR4cH6bMWrN/rkFl1rNYLGj8tbeUQDQEGBg03VpjXFEU5X45Ow0O4XA4pmV/6sxamfHCwsIwOPzp7ewlLCpsdHlrbSsr0ny34IQyuw0ODnL8xEkulV9Fr9dTsHwxK1cUoNVqvR2ach+klJw5c5ZjJ89isTgwBuh5cNM6lixZ7NH9qmStzHgajYbtm7bz3pfvE54VTkCQkc7GToLNgSxZtOTODzDHDAwMUFFZgcVqIS0ljYSEhHt6HIfDwZlzZzhbeg6T2URmUgab1m6adEasuWRkZIRf/+ZNRhyBJGUux2a1cvhECR3tHTz++A5vhzctRkZGkFLe16gMX3SusIgvj54nK6eAgAAjgwP9fLTvMAEBhjtWdLsfKlkrs0JGRgbfePIVSi6VMtDVT27GevJy86ZlRqiZpLq6mg8Ofogx0YjWX8eJ/SdZlLSQR7Y8ctd1sL868hUl7aVkb87BYDTQXNfM6x+9zjee+cacn62qoqKCoRENObm5zgWGAObnFVBy8Sjr13cTETF7p3odHBxk/xcHuVxRA0B6agLbHt06K56zlJITJ8+RMW8RAQFGAIKCQ0hKnc/xk2dVslYUd0RFRfHgRlUNdzJWq5WPD37MvI3ZhISHAGDPtXHhwAXm18+/qzHkJpOJ85UXWLYjH52f82ckOTOZ4cFhikuL2bhhoyeewozR2tZOYND45KTRaPAPCKG7e/Yma4fDwZtvvceg1UDu0o0IIWhurOfV19/m+9/5Bv7+t5YunUnsdjsDgyYyAoPHLQ8OCaOussKj+/ZoBzMhxKNCiAohRJUQ4k8nuP9FIUSJ699JIYRqs1QUD2lubkYTqhlN1ABanY6ojGgqqyvv6rH6+/vxC/YbTdTXhUeH0dbdNiXxzmRRkZEMDfWNWyalxGIeJCQkZJKtZr5r167R0WMmPWM+Wq2z02dSSjoWuz9Xr171dnj3TafTERcbSXdX+7jlHe2tpKV4dhIbjyVrIYQW+FdgG5ALPC+EyL1ptVpgo5RyMfCXwE89FY+izHVarRaH/da6Cg674647PYWEhGAbsGK1WMct7+3oJS7y9vMczwULFsxHL0xcq6/GbrdjsYxQebmYrPT4Kb2m39PTQ01NDV1dXVP2mPdjYGAAP3/jLcv1/oH09vluWdW7sfWhjTTVl9PS1IBpaJBrDTX0ddaxYf0aj+7Xk83gK4EqKWUNgBDiTWAXUH59BSnlyTHrnwZm5vx6ijIDxMfH4zfiR0dLB9HxzopZFvMIXTWdPPrIw3f1WEajkYIFBVw4doHM/HkYg4w01zUxVDfE0j1LPRD9zBIQEMDXv7aHr746wqULh9DpdBQsW8jGjRum5PHtdjufff4FF0sq0AeEYDEPMj87hV07H8PPz29K9nEvoqOjMQ/14nA4xg2lHB7qIT5udozMSE9P55WvPcWp0+doa7lMclIcax5/zuMdKz2ZrBOBa2NuNwK3q/T/TeCzie4QQnwH+A5ASopv19FVfNPAwABHTx6lvOYyfjod+QvyWbNqjVd/2KabVqvlqUef5O2979AS2oxWr8PUOsiGpRvuagal7u5umpqaiI+Jx1/vz4WTFxkyD5GZlMFju7fP6mbeuxEREcHTT+/GbrcjhLilDsD9OHuukNIrTeQt24hGo0FKScXlCxw7foIHN2+asv3crdjYWBbOT6P8UiGJyVloNBpaGmtIjA2eVcWJkpKSeObp6T239GSynqhr6YS1TYUQm3Em6wmnNJJS/hRXE3lBQcHMqo+qeJ3FYuH193+LNknL4h1LsNtsXCwupn1fO0/vetrb4U2r+Ph4fvfl71NXV4fVaiVpc9JdJdfDx45w5soZguOCsI3Ykb2SPTueJS5ONX1PxhPjqs8VFpOStmD0AEAIQXrGAs4VnmXzpo133bN/Ku3cuZ2kCxe5cLEMm93OuhXzWbGiYEoPVuYiTybrRiB5zO0koPnmlYQQi4GfAduklL5x4UWZVSoqKrAGW8hZ5Cpa4K9n4ZpFFO49R3t7+5wbF+zn58e8efPueru6ujrOVZ8jf/vy0Y5lbY1tfLj/Q77z0nfUj/E0Mo+M4HfTsESdnx6L1YqU0qvJWqvVUlCwnIKC2dHs7Ss8+e06B8wTQqQLIfTAc8DHY1cQQqQA7wMvSSnvrjuqorips6eTwKjxQy2EEARGBtLT0+OlqGae8spyYrJjx/UAj02KxaQZpr29/TZbKlMtb8E8mpvqxi1rbqpnfnaGOmiapTz2rkopbcAPgP3AZeBtKeUlIcT3hBDfc632Z0Ak8G9CiItCCDWdljLlosKjGOoaHLdMSomp2zTni3fcDYd0TJgIhIZpq4+sOG1YvxatvY+K8gs0NdZztaIE61ArDz04t8e3z2YeLYoipdwH7Ltp2U/G/P0t4FuejEFRsrOzOVZ4nOpL1aRmp2Kz2qgqvkpqeAqxsbHeDm/GmJ85nw9PfUR8avzoddju9i50Zt2ceR37+/upq6tzXiNOTycoKMgrcYSEhPDtb36d8vLLtLS2ERO9gLy8XIzGW4dN3SubzUZrays6nfP99WbT+u309PRQVnaJvr4BUlOTmD9//qzsOKrms1bmhP7+fo6cOEJ5TTk6rR/5ufmsW71WlSO9C1JKPj/wOWVNlwhJDMZqtjPSaubZbc+QnJx85weY4S5eLGbv54fxN4YjAetwD7t2bCUv7+byETPf1atX+fCT/dilHw6HjYjQAJ5+8nGioqK8Hdo49fX1/PatjzAExmAwGuntbicmwp+vvfCsT1VLGxkZoaamhtzcXDWftaLcTkhICDu37WQnO70dyowlhGDbw9tY0ryEa43XCDAEkLUla0rP5nxVb28vez8/TOaClaM1oU1Dg3z06QFSU1O8dobtCb29vfz/27v36CjrO4/j729uDAlISEIgiLlxCZIIiijUiLdeVDwKtrbVetTa2q5b29ru6Wl79px1u9uz57Rn95zd7e72Yru92K31ClaUopaCoAiCcg2ghEsgIXIJCQFCbjO//eN5CCEGMiQzmWfi53XOnMw88zzPfOeXZL7z+z3P8/09u2gJhaVXMPKiUQDUH9jPU88s5GsPPxSYY+LOORa//CoFheWMzvG+RIy/uIgd295l48ZNzJ59dYIj9Bw4cIAnn15ExAb2fxKMVheRpDF+/HhmXz2b6dOnfyQSNXhnwg/LyulK1ACZWSNIC2Wzd+/exAUWB9u27yCUNaYrUQMUjL+E4y2O2traBEZ2tqamJpqPt3Yl6tPGjitk+/u7EhTV2SKRCM8tXEzuuCmUTZs5oH0pWYuI9ME7XBjMY7axdqrlFGnpHx5CTk1Lp62tLQER9S49PZ2IC3/o5Mb29jYyQ8EYAv/ggw9oaXPk5A788lAlaxGRPpSUlNDW0kBr66muZadaTtLR2nhBs5Ulg5KSIo43HTwrCba3t9HR1tzvuc/70p9zp0aMGEHZxEJqdp+56re9vY1D9buZecX0WIY3ILE6LUzHrEVE+pCdnc28T13PkldeZ1hWDi7i6GhtZP5tnxxSx6sBiouLuXTyBLZtWUNu3gQ6OztoaqjlkzdVkpWVFdPXOnr0KMuXr2Tbe7sYNiyD2VfNoPKaa0hLiy41zZt3M88v/BNVG1eRlh6io+0EN869mkmTJsU0zv4aO3YswzMcRxsOk5M7ZkD70tngIiJRampqOuvSraFaBz0SiVBdXc3293aSkZ7OZRXTmDAhtrWwW1pa+MUvf0d65ljGTyiio6ODPdXbmFo6hgULLuxE0EOHDtHS0kJ+fn7gzqOora3lyacXQepIvvftB/t9NriStYiIDLq3317HyjXbmTx1RteySCRC1caVPPI39zN69OgERhdbra2tVFdXc9lll/U7WeuYtUgcnDhxgv3793Ps2LFEhyISSIcPN5A1MvusZSkpKWSERtDU1JSQmOIlFApRUVExoH3omLVIDEUiEZatWMaGnRsZNnoYrU2tlBdO45ZP3BL1cThJvIaGBnbt2k04HGbSpImMGTOw443J5MSJE+zbt4+UlBSKi4sJhUJxeZ38/Dy27doGFHUti0QitLeeGFK96ljRp4dIDL274V22HNrKzNtnkpqWRjgcpuqtKt5cs5rrr70u0eFJFN55dwNLX13J8BFjwIxlr6/lEzfMYc6c2efd7siRI3R0dJCfnx+XaTEHw6ZNm3l56XLSho0CIrjO1/jsp2+jtLQ05q9VUVHO6jXvULNnJ+MvLqK9o42a3duZUTE5ZjX7nXPs2rWLzVu20dnZSUX5VMrKypLy96NkLRJD67eup3T2RFL9XnRqaiqTr5jM+lfWcV3l3MDWVxZPc3MzS19dyeRpsxkWGg5Ae/tE/rJ8NVOmTCYnJ+dD2zQ2NrLwhZc4ePgYKalppKdGWHD7zUycOHGwwx+QxsZGXlq6nIlTz1Rpa25u4tmFL/Po178S8x728OHDeeC+z7Nq1Zts3baa4aFhXP+xGTGtPLZ8xeusfruKvPxCUlJDLHp5JVN37OTTd96RdP+LStYiMdTS2kJo+NkfasOGD6Oj88w8w+FwGDMLTNlGOaOmpoaMzNFdiRogI2MYoaw89u7d+6FkHYlEeOqZRbj0PMov967tbT7WyNPPv8TXvvpAUs3qtnNnNaGsvLOqtF10UTb1aVnU1NRQVlYW89fMzs7m9ttv4/Y4VAFubGxk9dpNXDq9krQ0b2KP/LHjqdq0mn379lFUVNTHHoJFyVokhsqKy6jbXUtp+ZleVd2eOkouLuH48eMsW7WM92t2YmZUTCznxrk3Bu5Sk4+ylJQUXC/TfTp6nx70wIEDNB1vZ9r0Mx/8F40aTSgrn6qqbVRWXhPXeGMpEolg1ssUqJZCOBxOQEQDU19fTyhzdFeiBq++febIXPbX1iVdstZXe5EYqpxdyak9LWxft436mnre37CDhq1HqLyqkj8sfJKmEceYdecsrrhjJvtdLc8tfq5f1ZskPkpKSoh0NHPieHPXspaTJ+hobez1uG1rayupaR+ejjEjYxgnW1riGmuslZaWcOr4YTra27uWnTrVQkfrsaRLbOCdgd3Z0fqh5eGONrIyh/eyRbCpZy0SQ9nZ2Xzp7i+xpWoL9YfqKRw9gemfn05tbS2dozopmeZ94KcCZTOnsuGVd6mrq4t5wQnpn8zMTD6z4FaeX7SElIyRgBFua2bB7Z/qtQBKQUEBHW3HaW9vIyPDq0ftnKO56RClJcEpeRmN/Px8bpg7ixWr3mL4yDwikQhtLQ3cMe/jMa9cNhiKiorICkF93T4KLi4E4GjDISIdzUyZMiXB0V04FUURGQRvrH6DHW3vMbH87DKI29/exvUl11FeXp6gyKQ3ra2tXbNpFRYWnvdQxZo1a3ltxVpy8gpJS0+n4XAtk4rGcNdnFiTleQmHDx9m9+49pKSkMGnSxKS+jKqhoYFFL7zEwSPNpKSkMCIznTvnz0vYl2Mz03zWIkGWn5fPug3roVtOds5x8vBJcmflJi4w6VUoFGLq1KlRrTtnzmzGjy9g85Yq2tramXvVXMrKypIyUQOMGTNmyFxXnpuby0NffoCGhgYikQi5ublJ+3tRshYZBKWlpYxafxHb11VRdGkJ4c4we7bupiSnmHHjxiU6vJg5evQodXV1ZGRkUFJSQkZGRqJDGhSFhYUUFhYmOgw5h9zc5P9CrGQtMgjS0tK45857WLNuLVtXbCU1JZVZZVcy+6rzF9pIJstXvM6bb20glJVDJNJBKsu49+5PD6kvIyKJomPWIjJge/fu5f+efompFXNIT/fOjj5y+CAtTXt55G8fStqhR5FYGsgxa/0HiciAbdv+HqNzJ3QlaoC8MWM5eSrMwYMHExiZyNCgYXARGbBwOIz11ns2I9JLkRFJXm1tbezZs4f29nYmTJjQawlWiT0laxEZsGmXlrGpainjCiZ0TZLQ1NhAKM3pmPUQUldXxx+fWUTEMklJSaf15F+ZWzmT66/TJDXxpmQtIgNWWlrKlTMm8c7GNxk+Mo9wZzuuo5l7Pjc/KWc4kg8Lh8M8+/yL5I6bSk6ud2lXR0cHq1a/RUlxsc6GjzMlaxEZMDPj1ltu5vIZ09m/fz+hUIhJkyap7vkQUl9fT2tHCiW5Z67BTk9PJzv3YrbveE/JOs6UrEUkZgoKCigoKEh0GBIH3qxxvTxhRiSSXFcVJSOdDS4iIn0qKCggLSVMU9PRrmXhcCfHjh7g0qnJV2s72ahnLSIifUpLS+OuO2/jqWf+xMH6UaSmptF68ihXz5yWlLNyJRslaxERiUpRURHfeOQhqquraW9v55JLLmHs2LGJDusjQclaRESilpmZyfTpyTX951CgY9YiIiIBp2QtIiIScErWIiIiAadkLSIiEnBK1iIiIgGnZC0iIhJwStYiIiIBp2QtIiIScErWIiIiAadkLSIiEnBK1iIiIgGnZC0iIhJwStYiIiIBp2QtIiIScErWIiIiAadkLSIiEnBK1iIiIgGnZC0iIhJwStYiIiIBp2QtIiIScErWIiIiAadkLSIiEnBK1iIiIgEX12RtZreY2XtmVm1m3+/leTOzn/jPbzazmfGMR0RkqHHOUV1dzeKXlrDkz69QU1OT6JAkDtLitWMzSwX+B/gkUAusM7MXnXPbuq12KzDZv80Gfub/FBGRPjjneHnJUjZW7SEnbwKRSBsbNi/m+soruPbaykSHJzEUt2QNXA1UO+d2A5jZU8B8oHuyng884ZxzwBozyzazAudcfRzjEhEZEurq6ti0dRflMypJSfEGSscVXMKKVW9QUVFOdnZ2YgOUmInnMPjFwP5uj2v9ZRe6joiI9KJm336Gj8jrStQA6enpDMscTV1dXQIjk1iLZ8/aelnm+rEOZvZV4Kv+wzYz2zrA2KR3ecCRRAcxBKld40Pteh7fHdjmatv4KOvvhvFM1rXAJd0eTwAO9GMdnHOPA48DmNl659ys2IYqoLaNF7VrfKhd40dtGx9mtr6/28ZzGHwdMNnMSswsA7gbeLHHOi8C9/tnhc8Bjul4tYiIyNni1rN2znWa2deBV4BU4NfOuSoze9h//ufAEmAeUA20AA/GKx4REZFkFc9hcJxzS/AScvdlP+923wGPXOBuH49BaNI7tW18qF3jQ+0aP2rb+Oh3u5qXL0VERCSoVG5UREQk4AKbrFWqND6iaNd7/fbcbGarzWxGIuJMRn21bbf1rjKzsJndNZjxJato2tXMbjCzjWZWZWavD3aMySiKz4JRZrbYzDb57apziqJgZr82s0PnusS437nLORe4G94JabuAUiAD2ARM67HOPODPeNdqzwHWJjruoN+ibNdrgNH+/VvVrrFr227r/RXvXI67Eh130G9R/s1m41VGLPQf5yc67qDfomzXvwd+7N8fAxwFMhIde9BvwHXATGDrOZ7vV+4Kas+6q1Spc64dOF2qtLuuUqXOuTVAtpkVDHagSabPdnXOrXbONfoP1+Bd+y59i+ZvFuAbwPPAocEMLolF065fABY65/YBOOfUtn2Lpl0dMNLMDBiBl6w7BzfM5OOcW4nXVufSr9wV1GStUqXxcaFt9mW8b4DStz7b1swuBu4Efo5EK5q/2SnAaDNbYWbvmNn9gxZd8oqmXf8buBSvUNUW4FHnXGRwwhvS+pW74nrp1gDErFSpnCXqNjOzG/GS9bVxjWjoiKZt/wP4nnMu7HVWJArRtGsacCXwcWA48JaZrXHOvR/v4JJYNO16M7ARuAmYCLxmZqucc81xjm2o61fuCmqyjlmpUjlLVG1mZtOBXwG3OucaBim2ZBdN284CnvITdR4wz8w6nXMvDEqEySnaz4IjzrmTwEkzWwnMAJSszy2adn0Q+JHzDrRWm9keYCrw9uCEOGT1K3cFdRhcpUrjo892NbNCYCFwn3omF6TPtnXOlTjnip1zxcBzwNeUqPsUzWfBn4C5ZpZmZpnAbGD7IMeZbKJp1314oxWY2Vi8SSh2D2qUQ1O/clcge9ZOpUrjIsp2fQzIBX7q9wA7nQr69ynKtpULFE27Oue2m9lSYDMQAX7lnNPMfOcR5d/rD4HfmtkWvKHb7znnNBNXH8zsj8ANQJ6Z1QL/CKTDwHKXKpiJiIgEXFCHwUVERMSnZC0iIhJwStYiIiIBp2QtIiIScErWIiIiAadkLTIA/uxZG81sq5k961/nG4/XmWVmP/Hv32Bm1/RjH98KSilOM/uBmX3Hv//PZvaJGOzzhP9zjH8pl8iQoWQtMjCnnHOXO+cqgHbg4Wg2MrMLqnHgnFvvnPum//AGvNnRoua/3peAJy9ku/PsK2acc4855/4Sw/0dBurNrDJW+xRJNCVrkdhZBUwysxwze8Gfq3aNX771dG/ycTN7FXjCzIrMbJm/3jK/ehxm9lm/p77JL515ujf9kpkV430h+Lbfo59rZnvMLN1f7yIz23v6cTc3Ae865zr99VaY2Y/N7G0ze9/M5vrLQ2b2GzPbYmYb/BrxmNkX/ZGDxcCr/uMXzJvveI+Zfd3M/s7fZo2Z5fjbfcXM1vnv5fneRh7M7Ldmdpc/erDRv20xM+c/P9HMlpo3SccqM5vqLy8xs7f8/f+wx25fAO4d0G9TJECUrEViwO9t3oo3O9E/ARucc9Px5gR+otuqVwLznXNfwJvV6Al/vT8AP/HXeQy42Tk3A7ij++s45/bizdr1736PfhWwArjNX+Vu4HnnXEePECuBd3osS3POXQ18C6/KEsAj/utcBtwD/M7MQv5zHwMecM7d5D+uwJue8mrgX4AW59wVwFvA6eH2hc65q/z3sh1vcphe+aMHlzvnLgeWAv/mP/U48A3n3JXAd4Cf+sv/E/iZc+4q4IMeu1sPzD3Xa4kkGyVrkYEZbmYb8ZLDPuB/8WYq+z2Ac+6vQK6ZjfLXf9E5d8q//zHODEv/njMznL2JV+bxK3ilIPvyK86ULHwQ+E0v6xQAh3ssW+j/fAco9u93j30HUIM3BSXAa8657vP0LnfOHfeHnY8Bi/3lW7rtr8LvDW/B6+mW9/VmzOxzwEzg+2Y2Am/I/1m/nX/hvxfwvoD80b//+x67OQSM7+u1RJJFIGuDiySRU35PsItZr/Nfnq7re/I8+3IAzrmHzWw2Xm95o5ldfp5tcM69aWbFZnY9kHqOutingFCPZW3+zzBnPgvON3dnz9jbut2PdHsc6ba/3wILnHObzOyLeMfbz8nMyvFGJq7zpxJNAZp6tnE356qXHMJ7zyJDgnrWIrG3Ev94qZndgDd9Y29zAK/GG7bGX/8Nf5uJzrm1zrnHgCOcPZ0ewHFgZI9lT+D1MnvrVYM3BD3pAmOfAhQC70Wx3bmMxDvZK50+jiH7ow9PAff7vXX8dttjZp/11zEzm+Fv8iZnt193UwBN5iFDhpK1SOz9AJhlZpuBHwEPnGO9bwIP+uvdBzzqL/9X/wSrrXjJc1OP7RYDd54+wcxf9gdgNGeGhXv6M3BdFLH/FEj1h62fBr7onGvrY5vz+QdgLfAasKOPdRcARcAvT59o5i+/F/iymW0CqoD5/vJHgUfMbB0wqse+bgReHkDcIoGiWbdEhgAzuwvvxLX7zrPOIuC7zrmdgxdZYvhn0c93zjUmOhaRWFCyFklyZvZfeGeiz3POvX+e9cqAsc65lYMWXAKY2Rig0jn3QqJjEYkVJWsREZGA0zFrERGRgFOyFhERCTglaxERkYBTshYREQk4JWsREZGAU7IWEREJuP8H7G4spnlm6/wAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans        # k-means clustering\n",
    "\n",
    "K = 5\n",
    "\n",
    "kmeans = KMeans(n_clusters=K, random_state=14, n_init = 100).fit(df.loc[:,['Norm_Porosity','Norm_AI']].values)\n",
    "df['kMeans'] = kmeans.labels_ + 1\n",
    "\n",
    "plt.subplot(111)                          # plot the training data and K prototypes\n",
    "plt.scatter(df_subset['Norm_Porosity'], df['Norm_AI'], c=df['kMeans'], alpha = 0.4, linewidths=1.0, edgecolors=\"black\")\n",
    "plt.title('Normalized Acoustic Impedence vs. Porosity with Final Prototypes'); plt.xlabel('Porosity (normalized)'); plt.ylabel('Acoustic impedance (normalized)')\n",
    "plt.xlim(0, 1); plt.ylim(0, 1)\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=1.0, top=1.1, wspace=0.2, hspace=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Selecting the Optimum Number of Clusters\n",
    "\n",
    "One method to assist with selection of the optimum number of clusters is the elbow method.\n",
    "\n",
    "* calculate the loss function for a range of k = 1,...,K cases and look for an inflection point, rapid reduction in slope."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "max_K = 50                                      # maximum number of clusters, k\n",
    "inertia = []\n",
    "for k in range(2,max_K+1):\n",
    "    kmeans_iter = KMeans(n_clusters=k, random_state=14, n_init = 10).fit(df.loc[:,['Norm_Porosity','Norm_AI']].values)\n",
    "    inertia.append(kmeans_iter.inertia_)\n",
    "\n",
    "plt.scatter(range(2,max_K+1),inertia,c='red',edgecolor='black',zorder=10)\n",
    "plt.plot(range(2,max_K+1),inertia,c='red',ls='--',zorder=1)\n",
    "plt.xlim(2,max_K); plt.xlabel('Number of Clusters'); plt.ylabel('Inertia'); plt.ylim(bottom=0)\n",
    "plt.vlines(13,0,np.max(inertia),color='black'); plt.grid(True); plt.title('k-Means Clustering Intertia vs. Number of Clusters')\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=1.0, top=1.1, wspace=0.2, hspace=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Comments\n",
    "\n",
    "There are so many more tests that one could attempt to gain experience with K-means clustering. I'll end here for brevity, but I invite you to continue. Consider, on your own:\n",
    "\n",
    "* change the number of $K$ prototypes\n",
    "\n",
    "* apply the original data (no normalization)\n",
    "\n",
    "* apply other data sets \n",
    "\n",
    "* attempting methods with supervised K-means classification.  \n",
    "\n",
    "I hope you found this tutorial useful. I'm always happy to discuss data analytics, geostatistics, statistical modeling, uncertainty modeling and machine learning,\n",
    "\n",
    "I have other demonstrations on the basics of working with DataFrames, ndarrays, univariate statistics, plotting data, declustering, data transformations, trend modeling and many other workflows available at https://github.com/GeostatsGuy/PythonNumericalDemos and https://github.com/GeostatsGuy/GeostatsPy. \n",
    "  \n",
    "I hope this was helpful,\n",
    "\n",
    "*Michael*\n",
    "\n",
    "#### The Author:\n",
    "\n",
    "### Michael Pyrcz, Associate Professor, University of Texas at Austin \n",
    "*Novel Data Analytics, Geostatistics and Machine Learning Subsurface Solutions*\n",
    "\n",
    "With over 17 years of experience in subsurface consulting, research and development, Michael has returned to academia driven by his passion for teaching and enthusiasm for enhancing engineers' and geoscientists' impact in subsurface resource development. \n",
    "\n",
    "For more about Michael check out these links:\n",
    "\n",
    "#### [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1)\n",
    "\n",
    "#### Want to Work Together?\n",
    "\n",
    "I hope this content is helpful to those that want to learn more about subsurface modeling, data analytics and machine learning. Students and working professionals are welcome to participate.\n",
    "\n",
    "* Want to invite me to visit your company for training, mentoring, project review, workflow design and / or consulting? I'd be happy to drop by and work with you! \n",
    "\n",
    "* Interested in partnering, supporting my graduate student research or my Subsurface Data Analytics and Machine Learning consortium (co-PIs including Profs. Foster, Torres-Verdin and van Oort)? My research combines data analytics, stochastic modeling and machine learning theory with practice to develop novel methods and workflows to add value. We are solving challenging subsurface problems!\n",
    "\n",
    "* I can be reached at mpyrcz@austin.utexas.edu.\n",
    "\n",
    "I'm always happy to discuss,\n",
    "\n",
    "*Michael*\n",
    "\n",
    "Michael Pyrcz, Ph.D., P.Eng. Associate Professor The Hildebrand Department of Petroleum and Geosystems Engineering, Bureau of Economic Geology, The Jackson School of Geosciences, The University of Texas at Austin\n",
    "\n",
    "I have other demonstrations on the basics of working with DataFrames, ndarrays, univariate statistics, plotting data, declustering, data transformations, trend modeling and many other workflows available at https://github.com/GeostatsGuy/PythonNumericalDemos and https://github.com/GeostatsGuy/GeostatsPy. \n",
    "  \n",
    "I hope this was helpful,\n",
    "\n",
    "*Michael*\n",
    "\n",
    "Michael Pyrcz, Ph.D., P.Eng. Associate Professor The Hildebrand Department of Petroleum and Geosystems Engineering, Bureau of Economic Geology, The Jackson School of Geosciences, The University of Texas at Austin\n",
    "\n",
    "#### More Resources Available at: [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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